Role: Agentic AI / GenAI Engineer

Experience: 6+ years in AI/ML/data science/software engineering, with 3 years in GenAI, LLM, RAG, conversational AI, or ML productionisation.

What we're looking for:

- Strong Python.

- API development using FastAPI, Flask, or similar.

- Understanding of LLMs, embeddings, vector search, prompt design, evaluation, and hallucination control.

- RAG architecture: ingestion, chunking, embeddings, retrieval, ranking, grounding, citations, evaluation.

- MLOps / LLMOps basics: model deployment, monitoring, evaluation, versioning, observability.

- Security and governance basics: IAM, PII handling, prompt injection risks, data leakage, approval workflows.

- Ability to build real working prototypes and production-ready services.

Technical Skills:

- Python, FastAPI, REST APIs, async processing.

- LLM application development.

- RAG implementation with vector databases.

- Prompt engineering, tool calling, function calling, structured outputs.

- LLM security: prompt injection, data leakage, access control, guardrails.

GCP Skills:

- VertexAI: Vector Search, Pinecone, Weaviate, FAISS, Chroma, pgvector, or equivalent.

- Gemini.

- Agent Orchestration using ADK: LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or equivalent.

- Cloud Run: Production deployment on Cloud Run, GKE, or equivalent.

- Evaluation using Vertex AI: Evals, RAGAS, custom eval frameworks, golden datasets, regression tests.

Nice to have:

- Google Agent Development Kit, Agent Engine / Gemini Enterprise Agent Platform, Model Armor, Agent observability and tracing, Multi-agent architecture, Human-in-the-loop approval flows, Enterprise knowledge graph / search integration.