About this role
Key Skills Required
• Core Engineering • Strong Python; solid OOP, typing, packaging • Test automation frameworks: pytest, Playwright/Cypress, Pact (contract testing) • Git, GitHub Actions / FluxCD, GitOps workflows • Kubernetes, Docker, Helm basics
• AI / LLM Engineering • Hands-on with LLM APIs (OpenAI, Anthropic, Azure OpenAI) and prompt engineering • RAG pipelines, embeddings, vector stores (FAISS, pgvector, or similar) • LangChain / LlamaIndex or equivalent orchestration frameworks • Fine-tuning and model adaptation basics (LoRA / PEFT awareness)
• Evaluation & Observability • LLM evaluation frameworks: Ragas, DeepEval, promptfoo, LangSmith • Metrics: groundedness, faithfulness, accuracy, latency, token cost • Golden dataset design and regression harness setup • Observability: OpenTelemetry, LangFuse, MLflow
• Guardrails & Trust • NeMo Guardrails, Guardrails AI, Llama Guard, or custom policy engines • Output schema validation (Pydantic, JSON schema) • PII / IP leakage detection, hallucination checks, rate-limit and cost controls
• Quality & DevOps • SonarQube, static analysis, code review automation • CI/CD integration of AI tests and evals • Familiarity with Speckit-driven and agentic delivery workflows
• Soft Skills • Collaborative mindset — works across GenAI Architect, domain developers, and QA • Clear documentation and evidence-based reporting • Comfortable operating in a hybrid Zensar + Vanderlande POD environment