Generative AI Engineer

Job Summary

We are looking for experienced Agentic AI Engineers with strong expertise in Retrieval-Augmented Generation (RAG), LLM-based AI Agents, and modern Generative AI frameworks. The ideal candidate should have started their career as a Data Engineer or Data Scientist and evolved into building production-grade GenAI applications.

Required Skills

• 5+ years of experience in Data Engineering, Data Science, Machine Learning, or AI Engineering.
• 2+ years of hands-on experience with Generative AI and Large Language Models.
• Strong experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines.
• Extensive experience building Agentic AI applications using multi-agent architectures.
• Hands-on experience with:
• LangChain
• LangGraph
• LlamaIndex
• CrewAI, AutoGen, or similar agent frameworks
• Experience integrating LLMs such as:
• OpenAI GPT
• Anthropic Claude
• Google Gemini
• Open-source models (Llama, Mistral, etc.)
• Strong Python programming skills.
• Experience with vector databases such as:
• Pinecone
• Weaviate
• Milvus
• ChromaDB
• FAISS
• Experience with embeddings, semantic search, document chunking, and retrieval optimization.
• Familiarity with prompt engineering and agent orchestration.
• Experience with REST APIs and microservices.
• Knowledge of cloud platforms (AWS, Azure, or GCP).
• Strong understanding of software engineering best practices, CI/CD, and Git.

Preferred Qualifications

• Background in Data Engineering or Data Science.
• Experience deploying AI applications in production.
• Experience with Kubernetes and Docker.
• Knowledge of ML pipelines and MLOps.
• Experience with knowledge graphs and enterprise search.
• Familiarity with AI evaluation frameworks, guardrails, and observability tools.

Responsibilities

• Design, develop, and deploy Agentic AI solutions for enterprise applications.
• Build scalable RAG pipelines for enterprise knowledge retrieval.
• Develop intelligent AI agents capable of planning, reasoning, and tool execution.
• Integrate LLMs with enterprise systems and APIs.
• Optimize prompt engineering, retrieval quality, and agent performance.
• Collaborate with Data Engineering, ML, and Product teams.
• Monitor, evaluate, and continuously improve AI system performance.
• Implement AI governance, security, and responsible AI practices.

Must-Have Skills

• Agentic AI
• Retrieval-Augmented Generation (RAG)
• AI Agents / Multi-Agent Systems
• LangChain
• LangGraph
• LlamaIndex
• Python
• Vector Databases
• OpenAI / Claude / Gemini APIs
• Prompt Engineering
• Data Engineering or Data Science background