Gen AI Engineer

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Job Summary

We are looking for a skilled Generative AI Engineer with strong hands-on expertise in Python, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI. The candidate will design, develop, and productionize enterprise-grade GenAI applications, including conversational AI, knowledge assistants, intelligent agents, and multi-step AI workflows.

Key Responsibilities

• Design and develop scalable Generative AI and LLM-based applications using Python.
• Build end-to-end RAG pipelines, covering document ingestion, text preprocessing, chunking, embeddings, vector indexing, retrieval, prompt construction, and LLM response generation.
• Develop Agentic AI workflows, including tool-calling agents, stateful agents, conditional routing, memory, and multi-agent orchestration.
• Implement advanced retrieval techniques such as semantic search, metadata filtering, hybrid retrieval, and reranking to improve response relevance. These patterns also appear in internally shared GenAI profiles. [Naukri_Sha...dav[9y_0m] | Word]
• Integrate LLMs with enterprise applications, APIs, databases, knowledge repositories, and external tools.
• Develop backend services and REST APIs using FastAPI/Flask.
• Implement prompt engineering, structured outputs, function/tool calling, and context-management techniques.
• Evaluate RAG and LLM solutions for accuracy, relevance, hallucination, latency, reliability, and cost.
• Implement logging, tracing, monitoring, exception handling, security, and observability for production GenAI applications.
• Work with product, engineering, research, and platform teams to deliver GenAI solutions end-to-end.

Mandatory Skills

• Strong hands-on programming experience in Python
• Strong understanding of Generative AI and LLM architecture
• Hands-on experience building RAG applications
• Experience with Agentic AI / AI Agents
• LangChain and/or LangGraph
• Prompt Engineering
• Embeddings and Vector Search
• Knowledge of semantic search, chunking strategies, retrieval optimization, and reranking
• Experience with LLM APIs and open-source/commercial models
• Git, Docker, and CI/CD fundamentals