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Job description

**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