Senior GenAI Quality Engineer and Solution Analyst

Experience: 6-9 Years
Role: Senior GenAI Quality Engineer and Solution Analyst

Key Skills:
• UI and API Quality Engineering

• Agentic and GenAI Application Testing

•Risk Based Test Automation and Observability

Responsibilities:
• Define and execute end to end test strategiescovering UI workflows, backend APIs, integrations and agentic interfaces.

• Test conversational and agentic behaviour including multi turn context, tool selection, tool inputs and outputs, state transitions, retries, timeouts, handoffs, approvals and recovery from partial failure.

• Validate GenAI responses for task completion, grounding, relevance, consistency, citation behaviour and safe failure, while recognising that outputs can be non deterministic.

• Perform functional, integration, regression, exploratory, negative, resilience and basic performance testing acrossapplication layers.

• Design API tests for contracts, authentication, authorisation, validation, error handling, idempotency, rate limits and downstream failures.

• Test UI behaviour across browsers and realistic user journeys, including loading states, interrupted sessions, feedback capture, accessibility basics and clear error communication.

• Create and maintain test data, reusable test scenarios and traceable evidence suitable for enterprise release governance.

• Use logs, traces, request and response payloads and observability tools to isolate defects and distinguish application, model, data, integration and platform issues.

• Automate the tests that materially reduce cycle time, manual effort or production risk, and keep unstable or low value scenarios out of the automation suite.

• Communicate defects and quality risks clearly to engineers, product owners, GenAI specialists, security teams and business stakeholders.

• Provide an evidence based release recommendation, including known limitations, residual risks and areas requiring monitoring.

• Partner with product owners, business users, architects, engineers and GenAI specialists to define the problem, target user journeys and expected business outcomes.

• Analyse proposed GenAI use cases and determine where deterministic application logic, retrieval, workflow orchestration, tool using agents or human approval should be used.

• Translate business requirements into end to end solution flows, functional requirements, interface behaviors, decision rules, acceptance criteria and non functional requirements.

• Map interactions across user interfaces, APIs, models, prompts, retrieval components, enterprise data sources, agent tools and downstream systems.

• Analyse solution options and document tradeoffs relating to quality, complexity, cost, latency, security, data access, maintainability and operational risk.

• Identify unclear ownership, missing controls, integration assumptions, failure scenarios and operational gaps before development begins.

• Support the design of human approval, fallback, escalation and exception handling paths for agentic solutions.

• Define measurable success criteria covering business outcomes, user experience, functional correctness, response quality, latency, reliability and safe failure.

• Maintain traceability from business need through solution requirement, implementation, evaluation scenario and release evidence.

• Facilitate structured design reviews and communicate findings using process flows, sequence diagrams, interface specifications, decision tables and concise solution documentation.

Requirements:

Core Requirements

• 5 to 8 years of experience in software quality engineering, test engineering or a similar hands on role covering complex applications.

• Strong experience testing web user interfaces, backend services and REST APIs.

• Hands on ability with API tools and automation frameworks such as Postman, REST Assured, pytest, Playwright, Cypress, Selenium or equivalent.

• Working knowledge of Java, Python, JavaScriptor TypeScript sufficient to build, review and troubleshoot test automation.

• Strong test analysis skills, including requirements review, risk assessment, boundary analysis, negative testing and traceability.

• Experience validating distributed systems and integrations, including asynchronous processing, queues, batch jobs and downstream dependencies.

• Ability to inspect logs, traces, networkcalls, payloads and database records to identify the actual failure point.

• Experience with Git, pull requests, CI/CDpipelines, test reporting and defect management tools.

• Understanding of security and privacy testing fundamentals, including access control, sensitive data handling, input validation and auditability.

• Strong stakeholder communication and the confidence to challenge weak designs, vague expected outcomes and premature release decisions.

• Ability to work in a fast moving environment where requirements and GenAI behaviour evolve.

Solution analysis and design expectations

• Experience analysing complex applications across user journeys, business processes, APIs, data flows and enterprise integrations.

• Ability to facilitate requirements discussions and convert ambiguous business needs into clear functional requirements, acceptance criteria and solution behaviours.

• Experience producing practical analysis artefacts such as process flows, sequence diagrams, context diagrams, interface specifications, decision tables and user stories.

• Ability to analyse solution alternatives and explain tradeoffs involving quality, cost, performance, security, operational support and delivery complexity.

• Understanding of application architecture concepts including synchronous and asynchronous integrations, event flows, authentication, authorisation, failure handling and system boundaries.

• Ability to distinguish problems that require GenAIfrom those better addressed through deterministic rules, search, workflow automation or conventional application logic.

• Confidence working with product, architecture, engineering, security, data and business stakeholders during discovery and solution design.

GenAI and AgenticTesting Expectations

• Practical understanding of LLM based applications, retrieval augmented generation, prompts, context windows, embeddings and tool using agents.

• Ability to test probabilistic systems using evaluation datasets, repeat runs, quality thresholds and evidence based acceptance criteria rather than brittle exact text matching.

• Experience validating grounded answers, citations, retrieval quality, hallucination risk, prompt injection resistance and safe handling of restricted or unsupported requests.

• Ability to test agent plans and execution paths, tool calls, memory and state, human approval checkpoints, fallback behaviour and termination conditions.

• Awareness of evaluation and observability tooling such as Langfuse, LangSmith, OpenTelemetry, Elastic, Splunk or equivalent.

Nice to Have

• Experience testing applications in banking, finance or another regulated enterprise environment.

• Experience with contract testing, service virtualisation, synthetic monitoring or performance testing tools.

• Experience with Kubernetes, OpenShift, AWShosted services or containerised deployments.

• Accessibility testing experience and familiarity with WCAG based checks.

• Experience building lean quality dashboards that show release risk, defect escape patterns, flaky tests and cycle time.

•Exposure to red teaming or adversarial testing of GenAI applications.