Role : Agentic AI / GenAI Engineer - GCP

We are looking for an experienced Agentic AI / GenAI Engineer to design, develop, and deploy production-grade AI solutions using LLMs, RAG, and agentic AI technologies. The ideal candidate should have strong hands-on experience in Python, GenAI/LLM application development, RAG architectures, and GCP.

Key Responsibilities :

- Design and develop scalable AI/ML solutions using LLMs and GenAI frameworks.

- Build production-grade LLM-powered applications and AI agents.

- Develop backend services and APIs using Python, FastAPI/Flask.

- Implement RAG pipelines including ingestion, chunking, embeddings, retrieval, and evaluation.

- Work on prompt engineering, tool/function calling, structured outputs, and hallucination control.

- Deploy and monitor AI solutions using MLOps/LLMOps practices.

- Implement LLM security, guardrails, PII protection, and prompt-injection prevention.

- Collaborate with cross-functional teams to design and deliver end-to-end AI solutions.

Mandatory Requirements :

- 6+ years of experience in AI/ML, Data Science, or Software Engineering.

- 3+ years of hands-on experience in GenAI, LLMs, RAG, or Conversational AI.

- Strong programming skills in Python.

- Hands-on experience with FastAPI / REST APIs / asynchronous processing.

- Strong experience in RAG implementation and vector databases.

- Understanding of embeddings, vector search, prompt engineering, and LLM evaluation.

- Experience with LLM security and guardrails.

- Experience with one or more agent/GenAI frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, or AutoGen.

- GCP - Mandatory.

- Hands-on experience with Google Cloud Platform (GCP) is mandatory.

- Experience with Vertex AI / Gemini or equivalent GCP AI platforms.

- Experience deploying applications using Cloud Run, GKE, or similar GCP services.

Good to Have :

- Google Agent Development Kit (ADK).

- Gemini Enterprise / Agent Engine.

- RAGAS, Evals, or custom evaluation pipelines.

- Model monitoring and observability.

- Multi-agent architecture.

- Human-in-the-loop workflows.

- Knowledge graph or enterprise search integration.

What We're Looking For :

- Strong problem-solving and analytical skills.

- Ability to take AI solutions from prototype to production.

- Experience working in Agile environments.

- Strong communication and collaboration skills.