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QA Automation Engineer, AI & Agentic Systems

Level: Senior IC
Location: Abu Dhabi

Role Summary

We are building agentic AI systems that read complex models, drawings, specifications, and regulatory documents to check compliance, plan and validate schedules, and support design.

This role exists to make those agents trustworthy enough to act on.

This is a senior, hands-on quality role weighted toward testing non-deterministic AI and agentic systems. That is where most of your time sits and where the hiring bar is highest. You will also own the broader quality surface: integration, API, and performance testing are part of the remit, not out of scope.

The differentiator we are hiring for is the ability to define what "good" looks like when a system reasons, calls tools, and can be wrong in subtle ways.

You will scope, build, and run the frameworks yourself, with the independence of a senior engineer, and decide what to build first.

Key Responsibilities

Agentic System Testing (primary focus)Tool-Use & Trajectory Evaluation: Test whether agents select the right tools for the right reasons and follow sound multi-step trajectories, not just whether the final answer looks plausible. Evaluate planning, intermediate steps, and recovery when a tool fails or returns nothing.Grounding & Citation Verification: Verify that agent claims are backed by cited evidence in the source model, drawing, or document, and that a stated location actually supports the answer, catching confident but unsupported outputs.Honest-Failure & Refusal Calibration: Assert that agents ask for clarification or state when something cannot be determined instead of inventing an answer, and build tests where the correct behaviour is refusal.Guardrail & Adversarial Testing: Probe prompt injection, jailbreaks, and instructions hidden inside ingested documents, ensuring the agent treats source content as untrusted data.Multi-Turn & State: Validate follow-ups, references to prior turns, and that conversational state carries correctly across a session. AI & LLM ValidationNon-Deterministic Testing: Architect automated frameworks that score generative-AI outputs for hallucination, consistency, and factual accuracy against gold-standard datasets, using LLM-as-judge methods calibrated against human judgement.Prompt & Model Regression: Design regression suites that catch prompt drift and model version drift, so changes to models or system instructions do not quietly degrade quality. Own the ground-truth and evaluation datasets these depend on. Live Production QualityContinuous Evaluation: Extend evaluation beyond pre-release into production, continuously scoring live agent outputs so quality is measured on real traffic, not only in the test environment.Monitoring & Alerting: Build quality monitoring that flags regressions, drift, and anomalous agent behaviour in production before users or customers do.Quality Incident Response: Triage quality incidents and close the loop, tracing failures in AI logic back to the specific model version or dataset that caused them and feeding fixes into the development cycle. Integration, API & PerformanceBackend, UI & API Testing: Build robust integration tests that validate API integration across services and key user-facing flows.Secure Gateway Validation: Automate testing of secure API gateways, verifying that role-based access controls and sensitive-data redaction logic work correctly before data reaches AI models.Performance & Load: Own performance test plans and their implementation using appropriate performance and load-testing technologies, validating latency, throughput, and stability under realistic load. Data, Traceability & Quality GatesData Validation: Use SQL and data-validation tooling to verify data quality across data platforms and vector databases, including the ground-truth and retrieval corpora the agents depend on.Requirements Traceability: Map test and evaluation cases to system requirements and user needs, producing the verification-and-validation evidence and quality reports needed to ship with confidence.Quality Gates: Enforce quality gates in CI/CD pipelines that prevent non-compliant models or code from merging and prepare readiness evidence for stage and release reviews.

Technical Requirements

AI Evaluation (core): Hands-on experience with LLM and agent-evaluation frameworks or custom Python evaluators, including LLM-as-judge techniques.Agent Observability: Experience tracing and debugging agent runs, including tool calls, intermediate steps, token usage, and latency, using modern agent observability and tracing technologies.Core Automation: Expert Python skills for custom test harnesses and evaluation tooling, plus experience with standard UI and API automation libraries.Performance Testing: Proven ability to craft and implement performance test plans using modern load and performance-testing tools.Data Validation: Proficiency with SQL and data-validation tools, with familiarity with vector databases and retrieval corpora.CI/CD Integration: Experience integrating automated tests and evaluations into CI/CD pipelines and enforcing quality gates.Test Management & Reporting: Experience managing test reports and artifacts and communicating results clearly.Version Control & QE Practices: Experience maintaining code-based frameworks in version control and applying modern quality-engineering practices for fast-paced teams, including shift-left testing, the test pyramid, and automation.Traceability Tools: Familiarity with requirements-management tools and linking results to requirement IDs.

Professional Qualifications

Experience: 5+ years in QA automation or quality engineering, with at least 2 years focused on testing ML models, LLM applications, or AI agents.Probabilistic-Systems Judgement: Able to define pass/fail criteria for systems whose outputs are not identical every run and to communicate confidence levels clearly to engineering leadership.Independent Operator: A senior doer who scopes and builds testing and evaluation frameworks with minimal direction and prioritises what matters first.Collaboration: Works closely with engineering and product, understands existing systems quickly, and moves fast.

Nice to Have

Industry Domain Experience: Familiarity with complex technical data and workflows, including models, drawings, specifications, compliance, and scheduling, is a strong plus.Formal V&V Exposure: Exposure to structured systems-engineering governance, stage-gate reviews, and formal verification and validation is welcome but not required. We value the discipline more than the certification.

GCS is acting as an Employment Business in relation to this vacancy.

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GCS is acting as an Employment Business in relation to this vacancy.

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GCS is acting as an Employment Business in relation to this vacancy.

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We are seeking an experienced Senior SDET / QA Automation Engineer to join our technology team. The ideal candidate will have a strong background in software quality assurance, test automation, application development, and database technologies. This role involves designing and executing test strategies, developing automation frameworks, collaborating with cross-functional teams, and ensuring the delivery of high-quality software solutions.

The successful candidate will possess a blend of QA expertise, software development skills, and database knowledge, with experience working across the full software development lifecycle.

Key ResponsibilitiesDevelop and execute comprehensive test plans, test cases, and test scripts to ensure software quality and reliability.Design, build, and maintain automated testing solutions and frameworks.Track, analyze, and report quality assurance metrics, including defects, test results, and test status.Collaborate with business analysts, developers, and stakeholders to understand business requirements and translate them into technical testing solutions.Identify, document, and assist in resolving software defects and production issues.Perform software validation, integration testing, regression testing, and system testing.Participate in quality audits and ensure adherence to established QA standards, methodologies, and best practices.Develop and maintain high-quality, well-documented code following established coding standards.Contribute to application design, coding, implementation, maintenance, and support activities.Design and optimize relational databases and develop SQL queries for data validation and testing.Support data acquisition, warehousing, and database maintenance initiatives.Participate in code reviews and promote best practices in software quality and testing.Evaluate new technologies, tools, and frameworks to improve efficiency and performance.Provide guidance and mentorship on quality assurance processes and testing methodologies.Required QualificationsBachelor's degree in Computer Science, Information Systems, Engineering, or a related field.8+ years of experience in software development, quality assurance, test automation, or related technical roles.Strong experience with one or more programming languages:Java.NET / C#Node.jsRubyStrong experience with SQL and relational databases.Experience creating and executing automated test scripts and test frameworks.Solid understanding of Software Development Life Cycle (SDLC) and Software Testing Life Cycle (STLC).Excellent object-oriented programming and design skills.Strong analytical, troubleshooting, and problem-solving abilities.Experience working in Agile development environments.Excellent communication and collaboration skills.

GCS is acting as an Employment Business in relation to this vacancy.

Read less

We are seeking an experienced Senior SDET / QA Automation Engineer to join our technology team. The ideal candidate will have a strong background in software quality assurance, test automation, application development, and database technologies. This role involves designing and executing test strategies, developing automation frameworks, collaborating with cross-functional teams, and ensuring the delivery of high-quality software solutions.

The successful candidate will possess a blend of QA expertise, software development skills, and database knowledge, with experience working across the full software development lifecycle.

Key ResponsibilitiesDevelop and execute comprehensive test plans, test cases, and test scripts to ensure software quality and reliability.Design, build, and maintain automated testing solutions and frameworks.Track, analyze, and report quality assurance metrics, including defects, test results, and test status.Collaborate with business analysts, developers, and stakeholders to understand business requirements and translate them into technical testing solutions.Identify, document, and assist in resolving software defects and production issues.Perform software validation, integration testing, regression testing, and system testing.Participate in quality audits and ensure adherence to established QA standards, methodologies, and best practices.Develop and maintain high-quality, well-documented code following established coding standards.Contribute to application design, coding, implementation, maintenance, and support activities.Design and optimize relational databases and develop SQL queries for data validation and testing.Support data acquisition, warehousing, and database maintenance initiatives.Participate in code reviews and promote best practices in software quality and testing.Evaluate new technologies, tools, and frameworks to improve efficiency and performance.Provide guidance and mentorship on quality assurance processes and testing methodologies.Required QualificationsBachelor's degree in Computer Science, Information Systems, Engineering, or a related field.8+ years of experience in software development, quality assurance, test automation, or related technical roles.Strong experience with one or more programming languages:Java.NET / C#Node.jsRubyStrong experience with SQL and relational databases.Experience creating and executing automated test scripts and test frameworks.Solid understanding of Software Development Life Cycle (SDLC) and Software Testing Life Cycle (STLC).Excellent object-oriented programming and design skills.Strong analytical, troubleshooting, and problem-solving abilities.Experience working in Agile development environments.Excellent communication and collaboration skills.

GCS is acting as an Employment Business in relation to this vacancy.

Read less

We are seeking an experienced Senior SDET / QA Automation Engineer to join our technology team. The ideal candidate will have a strong background in software quality assurance, test automation, application development, and database technologies. This role involves designing and executing test strategies, developing automation frameworks, collaborating with cross-functional teams, and ensuring the delivery of high-quality software solutions.

The successful candidate will possess a blend of QA expertise, software development skills, and database knowledge, with experience working across the full software development lifecycle.

Key ResponsibilitiesDevelop and execute comprehensive test plans, test cases, and test scripts to ensure software quality and reliability.Design, build, and maintain automated testing solutions and frameworks.Track, analyze, and report quality assurance metrics, including defects, test results, and test status.Collaborate with business analysts, developers, and stakeholders to understand business requirements and translate them into technical testing solutions.Identify, document, and assist in resolving software defects and production issues.Perform software validation, integration testing, regression testing, and system testing.Participate in quality audits and ensure adherence to established QA standards, methodologies, and best practices.Develop and maintain high-quality, well-documented code following established coding standards.Contribute to application design, coding, implementation, maintenance, and support activities.Design and optimize relational databases and develop SQL queries for data validation and testing.Support data acquisition, warehousing, and database maintenance initiatives.Participate in code reviews and promote best practices in software quality and testing.Evaluate new technologies, tools, and frameworks to improve efficiency and performance.Provide guidance and mentorship on quality assurance processes and testing methodologies.Required QualificationsBachelor's degree in Computer Science, Information Systems, Engineering, or a related field.8+ years of experience in software development, quality assurance, test automation, or related technical roles.Strong experience with one or more programming languages:Java.NET / C#Node.jsRubyStrong experience with SQL and relational databases.Experience creating and executing automated test scripts and test frameworks.Solid understanding of Software Development Life Cycle (SDLC) and Software Testing Life Cycle (STLC).Excellent object-oriented programming and design skills.Strong analytical, troubleshooting, and problem-solving abilities.Experience working in Agile development environments.Excellent communication and collaboration skills.

GCS is acting as an Employment Business in relation to this vacancy.

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QA Automation Engineer - AI & Agentic Systems

Level: Senior IC
Location: Remote

Role Summary

We are building agentic AI systems that can interpret complex data sources, documentation, and structured information to perform analysis, validation, and decision-support tasks. This role exists to make those agents trustworthy enough to act on.

This is a senior, hands-on quality role weighted toward testing non-deterministic AI and agentic systems. That is where most of your time sits and where the hiring bar is highest. You will also own the broader quality surface: integration, API, and performance testing are part of the remit, not out of scope.

The differentiator for this role is the ability to define what "good" looks like when a system reasons, calls tools, and can be wrong in subtle ways. You will scope, build, and run the frameworks yourself, with the independence of a senior engineer, and decide what to build first.

Key Responsibilities

Agentic System Testing (Primary Focus)Tool-Use & Trajectory Evaluation: Test whether agents select the right tools for the right reasons and follow sound multi-step trajectories, not just whether the final answer looks plausible. Evaluate planning, intermediate steps, and recovery when a tool fails or returns nothing.Grounding & Citation Verification: Verify that agent claims are backed by cited evidence in source data, documents, or knowledge bases, and that referenced locations actually support the answer, catching confident but unsupported outputs.Honest-Failure & Refusal Calibration: Assert that agents ask for clarification or respond appropriately when information cannot be confidently determined, rather than inventing answers.Guardrail & Adversarial Testing: Probe prompt injection, jailbreaks, and instructions hidden within ingested content, ensuring the agent treats source content as untrusted data.Multi-Turn & State: Validate follow-ups, references to prior turns, and that conversational state carries correctly across a session.

2. AI & LLM ValidationNon-Deterministic Testing: Architect automated frameworks that score generative AI outputs for hallucination, consistency, and factual accuracy against gold-standard datasets using LLM-as-judge methods calibrated against human judgement.Prompt & Model Regression: Design regression suites that catch prompt drift and model-version drift, ensuring changes to models or system instructions do not quietly degrade quality. Own the ground-truth and evaluation datasets these depend on. Live Production QualityContinuous Evaluation: Extend evaluation beyond pre-release into production, continuously scoring live agent outputs so quality is measured on real usage, not only in test environments.Monitoring & Alerting: Build quality monitoring that flags regressions, drift, and anomalous agent behaviour before users discover them.Quality Incident Response: Triage quality incidents and trace failures back to specific model versions, prompts, or datasets, feeding fixes into the development cycle. Integration, API & PerformanceBackend, UI & API Testing: Build robust integration tests that validate API integration across services and key user-facing flows.Secure Gateway Validation: Automate testing of secure API gateways, verifying that Role-Based Access Control (RBAC) and PII-redaction logic work correctly before data reaches AI models.Performance & Load: Own performance test plans and implementation (for example, Locust, JMeter, k6), validating latency, throughput, and stability under realistic load. Data, Traceability & Quality GatesData Validation: Use SQL and data-validation tooling (for example, Great Expectations) to verify data quality across data platforms and vector databases, including the ground-truth and retrieval corpora that agents depend on.Requirements Traceability: Map test and evaluation cases to system requirements and user needs, producing verification-and-validation evidence and quality reports needed to ship with confidence.Quality Gates: Enforce quality gates in GitLab CI/CD that prevent non-compliant models or code from merging and prepare readiness evidence for release reviews.

Technical Requirements

AI Evaluation (Core): Hands-on experience with LLM/agent evaluation frameworks (e.g., DeepEval, TruLens, RAGAS, or custom Python evaluators) and LLM-as-judge techniques.Agent Observability: Experience tracing and debugging agent runs, including tool calls, intermediate steps, token usage, and latency, using tools such as LangSmith, Langfuse, or OpenTelemetry-based tracing.Core Automation: Expert Python for custom test harnesses and evaluation tooling (Pytest), plus standard automation libraries (Selenium/Playwright for UI, Requests for API).Performance Testing: Proven ability to design and implement performance test plans (e.g., Locust, JMeter, k6).Data Validation: Proficiency with SQL and data-validation tools, and familiarity with vector databases and retrieval corpora.CI/CD Integration: Integrating automated tests and evaluations into GitLab CI/CD pipelines and enforcing quality gates.Test Management & Reporting: Managing test reports and artifacts (e.g., TestRail, Allure) and communicating results clearly.Version Control & QE Practices: Maintaining code-based frameworks in Git/GitLab and applying modern quality-engineering practices.Traceability Tools: Familiarity with requirements-management tools (e.g., Jira, Linear, Jama, Polarion) and linking results to requirement IDs.

Professional Qualifications

Experience: 5+ years in QA automation or quality engineering, with at least 2 years focused on testing ML models, LLM applications, or AI agents.Probabilistic Systems Judgement: Able to define pass/fail criteria for systems whose outputs are not identical every run and communicate confidence levels clearly to engineering leadership.Independent Operator: A senior individual contributor who scopes and builds testing and evaluation frameworks with minimal direction and prioritises what matters most.Collaboration: Works closely with engineering and product teams, understands existing systems quickly, and moves efficiently.

Nice to Have

Domain Experience: Familiarity with data-intensive workflows, complex documentation, compliance-focused environments, or highly regulated industries is advantageous.Formal V&V Exposure: Exposure to structured systems-engineering governance, stage-gate reviews, or formal verification and validation practices is beneficial but not required. We value the discipline more than the certification.

GCS is acting as an Employment Business in relation to this vacancy.

Read less

QA Automation Engineer - AI & Agentic Systems

Level: Senior IC
Location: Remote

Role Summary

We are building agentic AI systems that can interpret complex data sources, documentation, and structured information to perform analysis, validation, and decision-support tasks. This role exists to make those agents trustworthy enough to act on.

This is a senior, hands-on quality role weighted toward testing non-deterministic AI and agentic systems. That is where most of your time sits and where the hiring bar is highest. You will also own the broader quality surface: integration, API, and performance testing are part of the remit, not out of scope.

The differentiator for this role is the ability to define what "good" looks like when a system reasons, calls tools, and can be wrong in subtle ways. You will scope, build, and run the frameworks yourself, with the independence of a senior engineer, and decide what to build first.

Key Responsibilities

Agentic System Testing (Primary Focus)Tool-Use & Trajectory Evaluation: Test whether agents select the right tools for the right reasons and follow sound multi-step trajectories, not just whether the final answer looks plausible. Evaluate planning, intermediate steps, and recovery when a tool fails or returns nothing.Grounding & Citation Verification: Verify that agent claims are backed by cited evidence in source data, documents, or knowledge bases, and that referenced locations actually support the answer, catching confident but unsupported outputs.Honest-Failure & Refusal Calibration: Assert that agents ask for clarification or respond appropriately when information cannot be confidently determined, rather than inventing answers.Guardrail & Adversarial Testing: Probe prompt injection, jailbreaks, and instructions hidden within ingested content, ensuring the agent treats source content as untrusted data.Multi-Turn & State: Validate follow-ups, references to prior turns, and that conversational state carries correctly across a session.

2. AI & LLM ValidationNon-Deterministic Testing: Architect automated frameworks that score generative AI outputs for hallucination, consistency, and factual accuracy against gold-standard datasets using LLM-as-judge methods calibrated against human judgement.Prompt & Model Regression: Design regression suites that catch prompt drift and model-version drift, ensuring changes to models or system instructions do not quietly degrade quality. Own the ground-truth and evaluation datasets these depend on. Live Production QualityContinuous Evaluation: Extend evaluation beyond pre-release into production, continuously scoring live agent outputs so quality is measured on real usage, not only in test environments.Monitoring & Alerting: Build quality monitoring that flags regressions, drift, and anomalous agent behaviour before users discover them.Quality Incident Response: Triage quality incidents and trace failures back to specific model versions, prompts, or datasets, feeding fixes into the development cycle. Integration, API & PerformanceBackend, UI & API Testing: Build robust integration tests that validate API integration across services and key user-facing flows.Secure Gateway Validation: Automate testing of secure API gateways, verifying that Role-Based Access Control (RBAC) and PII-redaction logic work correctly before data reaches AI models.Performance & Load: Own performance test plans and implementation (for example, Locust, JMeter, k6), validating latency, throughput, and stability under realistic load. Data, Traceability & Quality GatesData Validation: Use SQL and data-validation tooling (for example, Great Expectations) to verify data quality across data platforms and vector databases, including the ground-truth and retrieval corpora that agents depend on.Requirements Traceability: Map test and evaluation cases to system requirements and user needs, producing verification-and-validation evidence and quality reports needed to ship with confidence.Quality Gates: Enforce quality gates in GitLab CI/CD that prevent non-compliant models or code from merging and prepare readiness evidence for release reviews.

Technical Requirements

AI Evaluation (Core): Hands-on experience with LLM/agent evaluation frameworks (e.g., DeepEval, TruLens, RAGAS, or custom Python evaluators) and LLM-as-judge techniques.Agent Observability: Experience tracing and debugging agent runs, including tool calls, intermediate steps, token usage, and latency, using tools such as LangSmith, Langfuse, or OpenTelemetry-based tracing.Core Automation: Expert Python for custom test harnesses and evaluation tooling (Pytest), plus standard automation libraries (Selenium/Playwright for UI, Requests for API).Performance Testing: Proven ability to design and implement performance test plans (e.g., Locust, JMeter, k6).Data Validation: Proficiency with SQL and data-validation tools, and familiarity with vector databases and retrieval corpora.CI/CD Integration: Integrating automated tests and evaluations into GitLab CI/CD pipelines and enforcing quality gates.Test Management & Reporting: Managing test reports and artifacts (e.g., TestRail, Allure) and communicating results clearly.Version Control & QE Practices: Maintaining code-based frameworks in Git/GitLab and applying modern quality-engineering practices.Traceability Tools: Familiarity with requirements-management tools (e.g., Jira, Linear, Jama, Polarion) and linking results to requirement IDs.

Professional Qualifications

Experience: 5+ years in QA automation or quality engineering, with at least 2 years focused on testing ML models, LLM applications, or AI agents.Probabilistic Systems Judgement: Able to define pass/fail criteria for systems whose outputs are not identical every run and communicate confidence levels clearly to engineering leadership.Independent Operator: A senior individual contributor who scopes and builds testing and evaluation frameworks with minimal direction and prioritises what matters most.

GCS is acting as an Employment Business in relation to this vacancy.

Read less