AES - DE - Generative AI Prompt Engineers

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

Experience in AI/ML Field, 4-7 years

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 + Client POD environment