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Automation Test Architect (Contract)

Hybrid

London

We're supporting a major technology transformation programme and are seeking an experienced Integration Test Architect to define and lead testing strategy across a complex microservices and distributed systems landscape.

This is a senior, hands-on architecture role requiring deep expertise in contract testing, integration testing and quality engineering across multiple technology stacks.

What You'll Be DoingDefine and own the end-to-end integration testing strategyDesign and implement scalable automated test frameworksLead adoption of consumer-driven contract testing using PactWork closely with architects, developers and platform teams to improve quality across distributed systemsEstablish testing standards, governance and best practice across multiple delivery teamsIdentify integration risks and ensure robust coverage across services and APIsMentor engineers and drive a quality-first engineering cultureEssential SkillsProven experience operating at Architect or Principal Test Architect levelExtensive experience in Integration Testing and Contract TestingStrong hands-on experience with PactDevelopment background in Java, Python and/or C++Experience testing microservices, APIs and distributed systemsStrong understanding of CI/CD and automated testing practicesAbility to influence senior stakeholders and engineering leadership teamsNice to HaveAWS cloud experienceEvent-driven architecture experiencePerformance and non-functional testing expertiseExperience within large-scale enterprise environmentsThe Candidate

You'll be a highly experienced consultant who can operate strategically at architecture level whilst remaining technically credible and hands-on when required. You'll have a track record of delivering testing strategies for complex integration-heavy environments and be recognised as a subject matter expert in modern quality engineering practices.

Automation Architect | Test Architect | Contract Testing | Pact | Java | Python | C++ | AWS | Microservices | QA Architect | Quality Engineering

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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.

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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.

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

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Software Tester (SC Cleared)

Drive quality. Accelerate delivery. Own automation. We're looking for a Software Tester who can build robust automated test solutions that boost efficiency, reduce manual effort, and ensure high‑quality software releases.

What You'll Do

Build Automation Strategy - Identify what to automate, prioritise test cases, and shape a scalable automation approach.

Develop Test Frameworks - Create and maintain automation frameworks using Selenium, Appium, TestNG, JUnit, or similar tools.

Write & Execute Scripts - Build reliable, maintainable automated tests across platforms, devices, and environments.

Integrate with CI/CD - Embed automated tests into pipelines using Jenkins, GitLab CI, Azure DevOps, etc.

Manage Test Data - Create and maintain test data sets and environments for automation.

Analyse & Report Results - Run automated suites, diagnose failures, and log defects clearly in JIRA.

Collaborate in Agile - Work closely with developers, product teams, and manual testers; contribute to sprint ceremonies.

Share Knowledge - Train team members and maintain documentation for frameworks, scripts, and processes.

What You'll Bring

Experience as an Automated Tester or Test Automation Engineer.

Strong skills in Selenium WebDriver, Appium, TestNG, JUnit, or similar tools.

Solid programming ability in Java, Python, C#, or JavaScript.

Experience with Git, Azure DevOps, or other version control systems.

Hands‑on CI/CD pipeline experience.

Excellent problem‑solving and attention to detail.

Strong communication and teamwork skills.

Agile experience (Scrum/Kanban).

Test automation certifications (ISTQB etc.) are a plus.

Why This Role?

You'll be at the heart of improving software quality and delivery speed. If you're passionate about automation, efficiency, and building reliable test solutions, this role gives you the platform to make a real impact.

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Role Overview

We are seeking an experienced Test Analyst/QA Tester with a background in Identity & Access Management (IAM) to test identity processing and user provisioning solutions across the identity lifecycle (Joiner, Mover, Leaver - JML).

The successful candidate will be responsible for validating that user accounts, access rights, and entitlements are correctly provisioned, updated, and removed across multiple integrated systems, including HR systems, Active Directory (AD/LDAP), IAM platforms, and business applications.

The role involves executing functional, integration, end-to-end, regression, and negative testing, validating identity data and business rules, performing SQL data validation, API testing using Postman, managing defects in Jira/ALM, and supporting UAT and release activities. The ideal candidate will work closely with developers, business analysts, and IAM engineers to ensure secure and accurate identity provisioning.

Key Technical SkillsEssentialIdentity & Access Management (IAM)Identity Lifecycle (Joiner, Mover, Leaver - JML)User Provisioning & De-provisioningFunctional, Integration, Regression & End-to-End TestingSQL (data validation, database queries)API Testing (Postman, REST APIs, JSON)Active Directory (AD) & LDAPRBAC (Role-Based Access Control)ABAC (Attribute-Based Access Control)Entitlements & Access GovernanceDefect Management (Jira, ALM)Test Case Design & Test ExecutionData Mapping & Identity Attribute ValidationStrong analytical and troubleshooting skillsDesirableSailPointSaviyntOktaMicrosoft Entra ID (Azure AD)SeleniumJava/Python

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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Broadcast Engineer

Are you an experienced Broadcast Master Control Operator who thrives in a fast-paced live television environment? We are seeking a dependable and detail-oriented Broadcast Master Control Engineer to support live Major League Baseball broadcasts from our Centennial, Colorado facility.

In this role, you'll help ensure seamless live broadcasts by monitoring video and audio quality, managing commercial breaks, adjusting playlists, and responding to real-time broadcast changes. This is an excellent opportunity to work with a leading sports media operation supporting live MLB programming.

ResponsibilitiesMonitor live video and audio feeds to ensure high-quality broadcastsPerform quality assurance (QA) on incoming and outgoing broadcast contentExecute commercial breaks accurately and on scheduleManage and adjust broadcast playlists in real time to accommodate schedule changes, weather delays, or live event updatesMonitor broadcast automation systems and troubleshoot issues as they ariseEnsure sponsorship elements and commercial inventory air according to scheduleVerify content readiness before air and maintain broadcast integrity throughout live eventsCommunicate with operations, engineering, and production teams to quickly resolve on-air issuesGenerate post-event and exception reports following broadcastsMaintain a high level of accuracy while working in a fast-paced live production environmentRequired QualificationsExperience working in Broadcast Master Control, Transmission, or Broadcast OperationsExperience supporting live television broadcastsHands-on experience with:Broadcast automation systemsPlaylist managementSignal monitoringContent quality control (QC)Commercial playoutStrong troubleshooting skills in a live broadcast environmentExcellent attention to detail and ability to multitask under pressureReliable, professional, and able to work independentlyWillingness to work evenings, weekends, and holidays as required by MLB schedulesPreferred QualificationsExperience supporting live sports broadcasts (MLB, NHL, NFL, NBA, NCAA, etc.)Experience with one or more of the following broadcast systems:FloricalImagine Communications (Harris/ADC)EvertzHarmonicPebbleGrass ValleyRoss VideoExperience creating operational or compliance reports following live broadcastsIdeal Candidate

We're looking for someone who has experience in a live broadcast environment and is comfortable making quick decisions under pressure. Successful candidates are dependable, organized, technically minded, and take pride in delivering flawless broadcasts for viewers and clients.

Why Join Us?Work on live Major League Baseball broadcastsJoin a collaborative broadcast operations teamGain experience in a high-profile live sports environmentOpportunity to work with industry-leading broadcast technology

If you have experience in Master Control, Broadcast Operations, or Live Television Production, we'd love to hear from you!

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

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