Site Reliability Engineer Jobs

Job Description Rejlers International Engineering Solutions is recognized for delivering reliable engineering and project services. With over 15 years in Abu Dhabi, UAE, our regional headquarters are based in Abu Dhabi City, and we also have an office in Navi Mumbai, India, supporting diverse operations throughout the area. We specialize in industries such as Oil & Gas, Refining, Petrochemical, Chemical, Renewable Energy, and Industrial Infrastructure—including Buildings, Power Distribution, and Telecommunications—serving clients across the UAE and Arabian Gulf. Our expertise in Industrial Digitization and AI solutions demonstrates our dedication to technological innovation and excellence. Backed by a parent company with over 80 years of engineering experience, we proudly bring Nordic engineering traditions to the region, combining advanced technology with a legacy of quality and reliability. We are now looking for Lead Engineer - Rotating and Packages with extensive experience in refinery and onshore / offshore oil and gas projects to join our Abu Dhabi Operations, UAE. The Lead Engineer - Rotating and Packages reports to Head of Discipline - Mechanical & Piping. Job Overview The Lead Engineer is responsible for leading the design, engineering, review, and technical assurance of all static equipment activities for ADNOC and EPC projects. The role ensures that engineering deliverables comply with ADNOC standards, international codes, project specifications, safety requirements, and quality objectives throughout the project lifecycle, including Conceptual, FEED, Detailed Engineering, EPC, Commissioning, and Start-up phases. The position provides technical leadership and guidance to a team of engineers and designers, ensuring safe, reliable, cost-effective, and operable designs for oil & gas, petrochemical, refinery, and energy projects. Key Responsibilities • Lead engineering activities for rotating equipment packages including: • Centrifugal compressors • Reciprocating compressors • Gas turbines • Steam turbines • Pumps (API and NFPA) • Blowers • Fans • Diesel engines • Package • Review and approve: • Datasheets • Specifications • Vendor documents • GA drawings • P&IDs • Calculations • Material requisitions • Ensure compliance with: • ADNOC standards • API standards • ASME codes • ISO requirements • Project specifications • Perform technical bid evaluations and vendor offer reviews. • Coordinate with: • Mechanical • Process • Electrical • Instrumentation • Pipeline • Civil departments • Participate in: • HAZOP • SIL reviews • Design reviews • FAT/SAT • Performance testing • Provide site engineering support during: • Installation • Alignment • Pre-commissioning • Commissioning • Startup • Troubleshoot vibration, seal failures, bearing issues, and operational problems. • Support reliability improvement and root cause failure analysis (RCFA). • Ensure timely project execution and engineering deliverables. Required Qualifications • Bachelor’s Degree in Mechanical Engineering. • 15+ years of rotating equipment engineering experience in Oil & Gas. • Minimum 5+ years in ADNOC projects or ADNOC group companies. • ADNOC approval preferred/mandatory. • Strong knowledge of: • API 610 • API 617 • API 618 • API 614 • API 682 • ISO 10439 • AGPS • Experience with EPC contractors and PMC environments. • Familiarity with: • ETAP • Bentley • AVEVA • SAP • MS Office Our work is guided by our vision: Home of the learning minds. We believe in continuous learning and development. We want to succeed both as individuals and as a company through a common goal: success through continuous learning. Do you want to join us? Please apply at once as we proceed with suitable candidates as soon as possible. Submit your application, CV, and salary expectation latest by 30 September 2026. Rejlers is a Nordic engineering consultancy firm, creating a sustainable future through knowledge. We are a trusted advisor for our customers in the areas of industry, buildings, energy and infrastructure. We operate globally, but know our local markets. With operations in Sweden, Finland, Norway and the United Arab Emirates, Rejlers has 2,700 experts in different technology areas. Over 1,100 employees work in over 20 locations in Finland and the United Arab Emirates. Rejlers’ share is listed on Mid Cap, Nasdaq Stockholm. Job Details Role Level: Not Applicable Work Type: Full-Time Country: United Arab Emirates City: Abu Dhabi Company Website: http://www.rejlers.com Job Function: Engineering Company Industry/ Sector: Other What We Offer About The Company Searching, interviewing and hiring are all part of the professional life. The TALENTMATE Portal idea is to fill and help professionals doing one of them by bringing together the requisites under One Roof. Whether you're hunting for your Next Job Opportunity or Looking for Potential Employers, we're here to lend you a Helping Hand. Report Disclaimer: talentmate.com is only a platform to bring jobseekers & employers together. Applicants are advised to research the bonafides of the prospective employer independently. We do NOT endorse any requests for money payments and strictly advice against sharing personal or bank related information. We also recommend you visit Security Advice for more information. If you suspect any fraud or malpractice, email us at [email protected].

Job Title: Senior Backend Developer Experience: 5-8 Years Location: Abu Dhabi (On-site) Employment Type: Full-Time Job Overview We are seeking a highly skilled Senior Backend Developer to design develop and scale robust backend systems for cloud-native applications. The ideal candidate has strong experience in Python and/or a solid understanding of microservices architecture and hands-on expertise with cloud deployment on Azure . You will work closely with cross-functional teams to build secure high-performance and scalable backend services in a fully remote environment. Key Responsibilities • Design develop and maintain scalable backend services using Python and/or • Build and consume RESTful APIs with a focus on performance reliability and security • Develop and integrate microservices-based architectures using Spring Boot where applicable • Containerize applications using Docker and manage deployments using Kubernetes • Design optimize and manage data storage solutions using SQL NoSQL and Vector Databases • Implement secure coding practices authentication authorization and data protection standards • Collaborate with DevOps teams to implement CI/CD pipelines and cloud deployments on Microsoft Azure • Monitor troubleshoot and optimize backend systems for scalability and high availability • Participate in code reviews architecture discussions and technical decision-making • Mentor junior developers and contribute to engineering best practices Required Skills & Qualifications • 58 years of professional experience in backend development • Strong proficiency in Python and/or • Solid experience building REST APIs and backend services • Hands-on experience with Spring Boot and Microservices architecture • Practical experience with Docker and Kubernetes in production environments • Strong knowledge of SQL databases (e.g. PostgreSQL MySQL) and NoSQL databases (e.g. MongoDB Cassandra) • Familiarity with Vector Databases (e.g. Pinecone Weaviate Milvus) is a strong plus • Experience implementing secure coding practices and handling application security concerns • Hands-on experience with Microsoft Azure (App Services AKS Azure DevOps etc.) • Strong understanding of system design performance optimization and scalability Nice to Have • Exposure to event-driven architectures and messaging systems • Experience with observability tools (logging monitoring tracing) • Knowledge of AI/ML-backed systems leveraging vector databases Required Experience: Senior IC

Anduril Industries is seeking a founding Site Reliability Engineer for the Advanced Capabilities division to design, build, and operate infrastructure for next‑generation wargaming simulations that run massive-scale autonomous systems in contested environments. You will maintain the simulation software stack, own underlying infrastructure, and build post-release automation. The role requires security clearance eligibility and strong cross-team communication. #J-18808-Ljbffr

Our Mission Rebuild how the world works, to make institutions work better for the people they serve. About Brain Co. Brain Co. builds AI-native operating systems for large, regulated institutions. Each system is built for a specific industry, powered by agents that push real workflows forward. Underneath it all is Atlas, our proprietary platform that keeps customers in control, secure by design, and never locked into one model. Why Now Brain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services. Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact. You'll work alongside exceptional peers on some of the hardest problems in applied AI. It’s the kind of work you'll still be proud of in ten years from now. Machine Learning Engineer, Applied AI About the Role So much of the work society depends on is still slower and harder than it should be. Permits take months. Claims sit unresolved. And AI hasn't changed that — because the bottleneck isn't the models. It's the institutional context AI needs to do the work: rules, history, relationships, and judgment scattered across people, documents, and legacy systems. BrainCo exists to fix that. We build agent-native operating systems for the institutions society depends on, and our products are the first of their kind in the world — we were the first, anywhere, to fully automate construction permitting, and we're now doing the same across insurance and other industries. There is no playbook here, because no one has built this before. As a Machine Learning Engineer on Applied AI, your work begins where the demo ends: getting a model to look impressive is the easy part; making it a production decision system an institution stakes its process on is the job. The problems come in every shape — custom vision model pipelines that check blueprints against building codes at 95%+ accuracy, agents that untangle policy stacks to reveal coverage gaps, systems that predict from clinical records whether a patient is on their care path — and you'll own them end-to-end, from ambiguous customer problem to the eval that catches a whole class of errors. This is frontier ML applied where it's hardest and matters most. The problems are underspecified, the documents are brutal, the accuracy bar is institutional-grade — and the feedback loops are real, because our systems move real workflows forward every day. Who We're Looking For You understand how machine learning actually works — not just the tooling, but the philosophy underneath: what a loss function really optimizes, how generalization breaks under distribution shift, why evaluation is where systems quietly go wrong. And you live at the bleeding edge of modern AI, with hard-won instincts for squeezing the most out of LLMs and agentic systems — prompting, fine-tuning, tool use, and reasoning. That combination is the job: you know when a fine-tuned segmentation model beats a VLM, when a rule engine beats both, and how to compose all three into a system more accurate than any single model. You treat frontier models as components to be measured, pushed, and engineered — never as magic. Most of all, you're energized by building things that have never existed, and comfortable when the problem, the data, and the definition of success all have to be invented at once. The Problems You'll Work On Composite AI systems and credit assignment. Our most demanding systems chain vision transformers, segmentation models, VLM reasoning, and rule engines. When the pipeline is wrong, which component failed? One of the most interesting open problems in applied ML. Document understanding beyond the frontier. Blueprints, site plans, policy stacks, contracts, clinical records — dense, multimodal documents that break off-the-shelf models. You'll build models that actually read them. Agents that learn from real work. Our deployments generate verified, ground-truth outcomes on every decision — reward signals most labs can only simulate. You'll help design the data, evals, and training loops to build and fine-tune agents on them. Evaluation as a product discipline. When a regulator has to trust your system, evals are the product. You'll build eval suites and failure-mode taxonomies rigorous enough to earn institutional sign-off. Institutional Intelligence that compounds. Every verified correction improves the system twice: the corrected fact percolates to every application, and the system that builds the intelligence learns to build it better. You'll work on both loops. In This Role, You Will: Turn ambiguity into shipped systems — from no problem statement, no labeled data, and no agreed definition of success, to well-posed ML problems and production deployments. Own AI systems end-to-end. There is no handoff: the person who trains the model owns its behavior in production. Work at the research frontier with production stakes, applying LLMs, RL fine-tuning, and agentic systems where the output is a decision an institution acts on. Work directly with the institutions we serve — permit reviewers, underwriters, compliance officers — to understand how decisions actually get made and ensure your systems change how the work gets done. Engineer for production reality, navigating accuracy, latency, cost, and reliability in environments far messier than any benchmark. Raise the bar across the company through design reviews, our internal paper club, and the shared playbook for AI systems institutions can trust.

Our Mission Rebuild how the world works, to make institutions work better for the people they serve. About Brain Co. Brain Co. builds AI-native operating systems for large, regulated institutions. Each system is built for a specific industry, powered by agents that push real workflows forward. Underneath it all is Atlas, our proprietary platform that keeps customers in control, secure by design, and never locked into one model. Why Now Brain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services. Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact. You'll work alongside exceptional peers on some of the hardest problems in applied AI. It’s the kind of work you'll still be proud of in ten years from now. Machine Learning Engineer, Applied AI About the Role So much of the work society depends on is still slower and harder than it should be. Permits take months. Claims sit unresolved. And AI hasn't changed that — because the bottleneck isn't the models. It's the institutional context AI needs to do the work: rules, history, relationships, and judgment scattered across people, documents, and legacy systems. BrainCo exists to fix that. We build agent-native operating systems for the institutions society depends on, and our products are the first of their kind in the world — we were the first, anywhere, to fully automate construction permitting, and we're now doing the same across insurance and other industries. There is no playbook here, because no one has built this before. As a Machine Learning Engineer on Applied AI, your work begins where the demo ends: getting a model to look impressive is the easy part; making it a production decision system an institution stakes its process on is the job. The problems come in every shape — custom vision model pipelines that check blueprints against building codes at 95%+ accuracy, agents that untangle policy stacks to reveal coverage gaps, systems that predict from clinical records whether a patient is on their care path — and you'll own them end-to-end, from ambiguous customer problem to the eval that catches a whole class of errors. This is frontier ML applied where it's hardest and matters most. The problems are underspecified, the documents are brutal, the accuracy bar is institutional-grade — and the feedback loops are real, because our systems move real workflows forward every day. Who We're Looking For You understand how machine learning actually works — not just the tooling, but the philosophy underneath: what a loss function really optimizes, how generalization breaks under distribution shift, why evaluation is where systems quietly go wrong. And you live at the bleeding edge of modern AI, with hard-won instincts for squeezing the most out of LLMs and agentic systems — prompting, fine-tuning, tool use, and reasoning. That combination is the job: you know when a fine-tuned segmentation model beats a VLM, when a rule engine beats both, and how to compose all three into a system more accurate than any single model. You treat frontier models as components to be measured, pushed, and engineered — never as magic. Most of all, you're energized by building things that have never existed, and comfortable when the problem, the data, and the definition of success all have to be invented at once. The Problems You'll Work On Composite AI systems and credit assignment. Our most demanding systems chain vision transformers, segmentation models, VLM reasoning, and rule engines. When the pipeline is wrong, which component failed? One of the most interesting open problems in applied ML. Document understanding beyond the frontier. Blueprints, site plans, policy stacks, contracts, clinical records — dense, multimodal documents that break off-the-shelf models. You'll build models that actually read them. Agents that learn from real work. Our deployments generate verified, ground-truth outcomes on every decision — reward signals most labs can only simulate. You'll help design the data, evals, and training loops to build and fine-tune agents on them. Evaluation as a product discipline. When a regulator has to trust your system, evals are the product. You'll build eval suites and failure-mode taxonomies rigorous enough to earn institutional sign-off. Institutional Intelligence that compounds. Every verified correction improves the system twice: the corrected fact percolates to every application, and the system that builds the intelligence learns to build it better. You'll work on both loops. In This Role, You Will: Turn ambiguity into shipped systems — from no problem statement, no labeled data, and no agreed definition of success, to well-posed ML problems and production deployments. Own AI systems end-to-end. There is no handoff: the person who trains the model owns its behavior in production. Work at the research frontier with production stakes, applying LLMs, RL fine-tuning, and agentic systems where the output is a decision an institution acts on. Work directly with the institutions we serve — permit reviewers, underwriters, compliance officers — to understand how decisions actually get made and ensure your systems change how the work gets done. Engineer for production reality, navigating accuracy, latency, cost, and reliability in environments far messier than any benchmark. Raise the bar across the company through design reviews, our internal paper club, and the shared playbook for AI systems institutions can trust.

Our Mission Rebuild how the world works, to make institutions work better for the people they serve. About Brain Co. Brain Co. builds AI-native operating systems for large, regulated institutions. Each system is built for a specific industry, powered by agents that push real workflows forward. Underneath it all is Atlas, our proprietary platform that keeps customers in control, secure by design, and never locked into one model. Why Now Brain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services. Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact. You'll work alongside exceptional peers on some of the hardest problems in applied AI. It’s the kind of work you'll still be proud of in ten years from now. Machine Learning Engineer, Applied AI About the Role So much of the work society depends on is still slower and harder than it should be. Permits take months. Claims sit unresolved. And AI hasn't changed that — because the bottleneck isn't the models. It's the institutional context AI needs to do the work: rules, history, relationships, and judgment scattered across people, documents, and legacy systems. BrainCo exists to fix that. We build agent-native operating systems for the institutions society depends on, and our products are the first of their kind in the world — we were the first, anywhere, to fully automate construction permitting, and we're now doing the same across insurance and other industries. There is no playbook here, because no one has built this before. As a Machine Learning Engineer on Applied AI, your work begins where the demo ends: getting a model to look impressive is the easy part; making it a production decision system an institution stakes its process on is the job. The problems come in every shape — custom vision model pipelines that check blueprints against building codes at 95%+ accuracy, agents that untangle policy stacks to reveal coverage gaps, systems that predict from clinical records whether a patient is on their care path — and you'll own them end-to-end, from ambiguous customer problem to the eval that catches a whole class of errors. This is frontier ML applied where it's hardest and matters most. The problems are underspecified, the documents are brutal, the accuracy bar is institutional-grade — and the feedback loops are real, because our systems move real workflows forward every day. Who We're Looking For You understand how machine learning actually works — not just the tooling, but the philosophy underneath: what a loss function really optimizes, how generalization breaks under distribution shift, why evaluation is where systems quietly go wrong. And you live at the bleeding edge of modern AI, with hard-won instincts for squeezing the most out of LLMs and agentic systems — prompting, fine-tuning, tool use, and reasoning. That combination is the job: you know when a fine-tuned segmentation model beats a VLM, when a rule engine beats both, and how to compose all three into a system more accurate than any single model. You treat frontier models as components to be measured, pushed, and engineered — never as magic. Most of all, you're energized by building things that have never existed, and comfortable when the problem, the data, and the definition of success all have to be invented at once. The Problems You'll Work On Composite AI systems and credit assignment. Our most demanding systems chain vision transformers, segmentation models, VLM reasoning, and rule engines. When the pipeline is wrong, which component failed? One of the most interesting open problems in applied ML. Document understanding beyond the frontier. Blueprints, site plans, policy stacks, contracts, clinical records — dense, multimodal documents that break off-the-shelf models. You'll build models that actually read them. Agents that learn from real work. Our deployments generate verified, ground-truth outcomes on every decision — reward signals most labs can only simulate. You'll help design the data, evals, and training loops to build and fine-tune agents on them. Evaluation as a product discipline. When a regulator has to trust your system, evals are the product. You'll build eval suites and failure-mode taxonomies rigorous enough to earn institutional sign-off. Institutional Intelligence that compounds. Every verified correction improves the system twice: the corrected fact percolates to every application, and the system that builds the intelligence learns to build it better. You'll work on both loops. In This Role, You Will: Turn ambiguity into shipped systems — from no problem statement, no labeled data, and no agreed definition of success, to well-posed ML problems and production deployments. Own AI systems end-to-end. There is no handoff: the person who trains the model owns its behavior in production. Work at the research frontier with production stakes, applying LLMs, RL fine-tuning, and agentic systems where the output is a decision an institution acts on. Work directly with the institutions we serve — permit reviewers, underwriters, compliance officers — to understand how decisions actually get made and ensure your systems change how the work gets done. Engineer for production reality, navigating accuracy, latency, cost, and reliability in environments far messier than any benchmark. Raise the bar across the company through design reviews, our internal paper club, and the shared playbook for AI systems institutions can trust.

Our Mission Rebuild how the world works, to make institutions work better for the people they serve. About Brain Co. Brain Co. builds AI-native operating systems for large, regulated institutions. Each system is built for a specific industry, powered by agents that push real workflows forward. Underneath it all is Atlas, our proprietary platform that keeps customers in control, secure by design, and never locked into one model. Why Now Brain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services. Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact. You'll work alongside exceptional peers on some of the hardest problems in applied AI. It’s the kind of work you'll still be proud of in ten years from now. Machine Learning Engineer, Applied AI About the Role So much of the work society depends on is still slower and harder than it should be. Permits take months. Claims sit unresolved. And AI hasn't changed that — because the bottleneck isn't the models. It's the institutional context AI needs to do the work: rules, history, relationships, and judgment scattered across people, documents, and legacy systems. BrainCo exists to fix that. We build agent-native operating systems for the institutions society depends on, and our products are the first of their kind in the world — we were the first, anywhere, to fully automate construction permitting, and we're now doing the same across insurance and other industries. There is no playbook here, because no one has built this before. As a Machine Learning Engineer on Applied AI, your work begins where the demo ends: getting a model to look impressive is the easy part; making it a production decision system an institution stakes its process on is the job. The problems come in every shape — custom vision model pipelines that check blueprints against building codes at 95%+ accuracy, agents that untangle policy stacks to reveal coverage gaps, systems that predict from clinical records whether a patient is on their care path — and you'll own them end-to-end, from ambiguous customer problem to the eval that catches a whole class of errors. This is frontier ML applied where it's hardest and matters most. The problems are underspecified, the documents are brutal, the accuracy bar is institutional-grade — and the feedback loops are real, because our systems move real workflows forward every day. Who We're Looking For You understand how machine learning actually works — not just the tooling, but the philosophy underneath: what a loss function really optimizes, how generalization breaks under distribution shift, why evaluation is where systems quietly go wrong. And you live at the bleeding edge of modern AI, with hard-won instincts for squeezing the most out of LLMs and agentic systems — prompting, fine-tuning, tool use, and reasoning. That combination is the job: you know when a fine-tuned segmentation model beats a VLM, when a rule engine beats both, and how to compose all three into a system more accurate than any single model. You treat frontier models as components to be measured, pushed, and engineered — never as magic. Most of all, you're energized by building things that have never existed, and comfortable when the problem, the data, and the definition of success all have to be invented at once. The Problems You'll Work On Composite AI systems and credit assignment. Our most demanding systems chain vision transformers, segmentation models, VLM reasoning, and rule engines. When the pipeline is wrong, which component failed? One of the most interesting open problems in applied ML. Document understanding beyond the frontier. Blueprints, site plans, policy stacks, contracts, clinical records — dense, multimodal documents that break off-the-shelf models. You'll build models that actually read them. Agents that learn from real work. Our deployments generate verified, ground-truth outcomes on every decision — reward signals most labs can only simulate. You'll help design the data, evals, and training loops to build and fine-tune agents on them. Evaluation as a product discipline. When a regulator has to trust your system, evals are the product. You'll build eval suites and failure-mode taxonomies rigorous enough to earn institutional sign-off. Institutional Intelligence that compounds. Every verified correction improves the system twice: the corrected fact percolates to every application, and the system that builds the intelligence learns to build it better. You'll work on both loops. In This Role, You Will: Turn ambiguity into shipped systems — from no problem statement, no labeled data, and no agreed definition of success, to well-posed ML problems and production deployments. Own AI systems end-to-end. There is no handoff: the person who trains the model owns its behavior in production. Work at the research frontier with production stakes, applying LLMs, RL fine-tuning, and agentic systems where the output is a decision an institution acts on. Work directly with the institutions we serve — permit reviewers, underwriters, compliance officers — to understand how decisions actually get made and ensure your systems change how the work gets done. Engineer for production reality, navigating accuracy, latency, cost, and reliability in environments far messier than any benchmark. Raise the bar across the company through design reviews, our internal paper club, and the shared playbook for AI systems institutions can trust.

Your mission is to make DigitalZone able to scale. You will build the platform's capacity to absorb campaign-level traffic spikes, and you will give every engineering team the tools, standards, and practices to load- and failure test their own systems. This is an enablement role at its core: you raise the reliability bar across the org by building capability, not by owning every service yourself. What you'll do Build the platform's scalability foundation: capacity planning, autoscaling, caching, queueing, and graceful degradation designed for large campaign spikes rather than steady-state load. Establish load and failure testing as a standard engineering practice, giving teams the frameworks, tooling, and runbooks to test their own services and act on the results. Own SLOs, error budgets, and the observability stack (metrics, logs, traces, alerting) across TypeScript, Go, and PHP/Laravel services, and standardize how teams instrument for scale. Harden Postgres and AWS infrastructure for performance and availability, and reduce toil through automation and IaC. Lead incident response and blameless postmortems, and drive the systemic fixes upstream into design and campaign planning so reliability is built in, not bolted on. Partner with engineering teams early on capacity and resilience, acting as the multiplier that makes them self-sufficient at scaling their own systems. Immediate, large-scale impact on a high-growth business. Top-of-the-market compensation packages. Work alongside top regional talent, with team members from Talabat, Careem, Etisalat, and more. What you'll bring 5+ years in SRE, platform, or backend engineering, with strong production ownership of large-scale systems operating at 10s of thousands of requests per minute. A track record of scaling systems through real traffic spikes, and of designing and running load and failure testing programs that other teams adopted. Deep AWS experience and a solid grasp of Postgres performance and scaling. Fluency with observability tooling and infrastructure-as-code, plus scripting in Go, TypeScript, or similar. A calm, systematic approach to incidents, and the communication skills to influence and enable other teams rather than gatekeep. •

LocationDubai, United Arab Emirates - Client SiteJob CategoryInformation Technology (IT) & SoftwareEngineering & TechnicalJob OverviewAn opportunity is available for an experienced API QA Engineer to work at a client site in Dubai, UAE. This position is suitable for quality assurance professionals with three to five years of relevant experience and strong expertise in API testing, test automation, REST API validation, OpenAPI and Swagger contract validation, performance testing, and CI/CD API QA Engineer will be responsible for ensuring the quality, reliability, functionality, and performance of backend services and APIs through comprehensive manual and automated testing. The role requires hands-on experience with Postman, Robot Framework, REST APIs, API schemas, authentication mechanisms, error handling, load testing, stress testing, and continuous integration and continuous delivery pipelines.This position offers valuable career growth and professional development opportunities in software quality assurance, API automation, backend testing, performance engineering, and DevOps-integrated quality engineering. Continued training and relevant certification in test automation, API testing, performance testing, and CI/CD technologies can further support long-term career progression.Key ResponsibilitiesTest backend services and APIs to ensure functionality, reliability, security, and performance.Design, develop, and maintain comprehensive API test scenarios and test cases.Perform API testing using Postman, including collections, environments, and scripted assertions.Develop and maintain automated test suites using Robot Framework.Validate REST API schemas, status codes, authentication mechanisms, responses, and error handling.Perform OpenAPI and Swagger contract validation to ensure API implementations comply with documented functional, integration, regression, negative, and end-to-end API testing.Conduct load and stress testing to assess API performance, scalability, and stability.Identify, document, track, and verify software defects throughout the testing lifecycle.Integrate automated testing into CI/CD pipelines using Jenkins, GitLab CI, or similar technologies.Analyze test results, troubleshoot failures, and collaborate with developers to identify root causes.Maintain accurate testing documentation, automation scripts, and quality reports.Contribute to continuous improvement of API testing strategies, automation frameworks, and quality assurance processes.Collaborate with developers, DevOps engineers, product teams, and other stakeholders to support reliable software delivery.Requirements & QualificationsThree to five years of relevant professional experience in software quality assurance, API testing, or test hands-on expertise in API testing using Postman.Experience working with Postman collections, environments, and scripted assertions.Practical experience developing test automation using Robot Framework.Strong understanding of REST API testing concepts and methodologies.Experience validating API schemas, HTTP status codes, authentication mechanisms, responses, and error handling.Knowledge of OpenAPI and Swagger contract conducting API load and stress testing.Understanding of API reliability, scalability, and performance requirements.Experience integrating automated tests into CI/CD pipelines.Familiarity with Jenkins, GitLab CI, or similar CI/CD troubleshooting, analytical, and root-cause analysis capabilities.Excellent communication and collaboration skills.Relevant professional certification or specialized training in software testing, API testing, test automation, performance testing, or DevOps can further support professional development and career growth.Salary, Benefits & Career GrowthThe salary for this position is AED 7,000 to AED 10,000 per month, plus benefits.This opportunity provides experienced QA professionals with exposure to modern API testing, backend service validation, test automation, contract testing, performance engineering, and CI/CD-integrated quality assurance practices.The successful candidate can strengthen technical expertise in Postman, Robot Framework, REST APIs, OpenAPI, Swagger, load testing, stress testing, Jenkins, GitLab CI, and automated software delivery. This experience can support future career progression into roles such as Senior QA Automation Engineer, API Test Automation Engineer, Software Development Engineer in Test, Performance Test Engineer, QA Lead, or Quality Engineering Manager.Continued professional development through relevant training and certification in software testing, API automation, performance engineering, and DevOps technologies can further enhance long-term career growth. #J-18808-Ljbffr

The Electrical Engineering Manager will be responsible for leading all electrical engineering activities during the construction, installation, commissioning, and operational phases of the BOPP manufacturing plant. The role involves coordinating with consultants, OEMs, contractors, and internal departments to ensure the safe, reliable, and efficient execution of all electrical works while maintaining compliance with applicable standards and project requirements.Key Responsibilities Lead and manage all electrical engineering activities during plant construction, installation, commissioning, and operations. Review and interpret electrical drawings, single line diagrams (SLDs), BOQs, technical specifications, cable schedules, layouts, and vendor documentation. Supervise the installation, testing, commissioning, and energization of electrical systems, power distribution networks, and plant utilities. Coordinate with consultants, OEMs, contractors, and cross-functional teams to ensure timely execution of electrical works and resolution of technical issues. Review technical submissions, evaluate vendors, support procurement activities, and ensure compliance with project specifications. Oversee HT/LT systems, transformers, substations, etc and ensure reliable plant power distribution. Develop and implement preventive, predictive, and breakdown maintenance strategies to maximize equipment reliability and plant availability after commissioning. Ensure compliance with HSE standards, electrical codes, statutory regulations, and company policies while maintaining safe work practices. Manage electrical budgets, contractors, manpower, maintenance schedules, spare parts inventory, and support energy optimization initiatives. Prepare engineering reports, maintenance documentation, SOPs, as-built drawings, commissioning records, and support continuous improvement across the plant. Qualifications Bachelor's Degree in Electrical Engineering. Professional certifications related to Electrical Engineering, Project Management, or Maintenance Management will be an added advantage. Experience Minimum 10 years of experience in industrial manufacturing or process industries. Minimum 5 years of experience in a managerial or leadership role. Proven experience in plant construction, electrical installation, testing, commissioning, and maintenance. Experience in BOPP, BOPET, Flexible Packaging, Plastics, Petrochemical, FMCG, or similar manufacturing industries will be preferred. Required Skills Strong knowledge of industrial electrical systems, power distribution, and electrical engineering principles. Ability to read and interpret electrical drawings, SLDs, BOQs, technical specifications, and cable schedules. Experience in project execution, contractor management, vendor coordination, and procurement support. Sound knowledge of HT/LT systems, transformers, substations, MCCs, PCCs, VFDs, PLC/SCADA coordination, and industrial utilities. Strong troubleshooting, analytical, and problem-solving skills. Excellent leadership, planning, communication, and team management abilities. Proficiency in Microsoft Office; knowledge of AutoCAD, MS Project, and electrical design/maintenance software will be an added advantage. Working Conditions Full-time on-site role based at the LT FILMS INDUSTRIES LLC manufacturing facility. The role requires regular site inspections, coordination with consultants, contractors, utility providers, and OEM engineers, and active involvement during construction, commissioning, and plant operations. Good communication skills and must be able to lead the team