Senior Software Tester (Python, AI/LLM Pipelines) at SopraSteria1

We are looking for a Senior Software Tester with 10+ years of experience in testing Python-based backend systems, AI/LLM pipelines, API layers, and automated testing infrastructures. You will own the quality lifecycle end-to-end, from API entrypoints to full AI pipeline validation, helping ensure reliability, performance, and observability across production-grade systems. This role is intended for an expert tester capable of working autonomously, designing advanced automation strategies, and guiding teams in best practices for resilience, observability, and continuous validation. Key Responsibilities 1. Testing & Quality Assurance • Design, implement, and maintain automated test suites using pytest (fixtures, parametrization, asyncio). • Build end-to-end test coverage across UI, API layers, backend services, and AI/LLM pipelines. • Implement browser and API E2E testing using Playwright. • Develop contract and integration tests for REST and gRPC services. • Detect and remediate flaky tests, ensuring long-term stability. • Perform failure-injection testing (timeouts, malformed payloads, empty-response handling). • Validate auth flows: SSO, OAuth2, token-based access.2. Backend & Infrastructure Testing • Deep testing experience with Python systems: async patterns, package design, CLI tooling. • Validate backend services built with FastAPI or equivalent. • Work with containerized environments (Docker) and Kubernetes-based E2E topologies mirroring production. • Integrate and validate CI/CD flows using GitHub Actions or GitLab CI.3. Observability & Metrics • Implement and validate OpenTelemetry instrumentation (traces, logs, metrics). • Build latency and SLO/SLA assertions directly into test suites. • Use Jaeger, Grafana, or similar platforms to analyse distributed traces and test regressions. • Produce trace-based regression reports for AI pipeline executions.4. End‑to‑End Ownership • Ensure full lifecycle coverage from API trigger → backend logic → AI pipeline → validated output. • Maintain containerized E2E test environments. • Ensure reproducibility and alignment with production architecture.Expected Deliverables • Automated test suites achieving ≥80% critical-path coverage. • Comprehensive E2E test plans covering core user journeys, edge cases, and failure modes. • Trace-based regression analysis for AI/LLM workloads. • Documentation: • Testing strategies • Test case catalogues • Execution reporting