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Metro locations and broad in-demand GenAI skills raise competition, though company isn't Tier-1.
Specialized GenAI and enterprise integration experience somewhat limits transferability across industries.
Explicit 6-12 years plus mandatory LLM/agentic production experience and specific tech stack increases shortlisting strictness.
Design, develop, and deploy production-grade GenAI and Agentic AI solutions, including multi-agent architectures with tool use, memory, and planning capabilities.
Build and optimize Retrieval-Augmented Generation (RAG) pipelines involving document processing, retrieval strategies, vector databases, and context management.
Develop scalable backend services and APIs integrating AI solutions with enterprise platforms, while ensuring secure cloud deployment and LLM performance management.
6-12 years of experience in production-grade GenAI/Agentic AI engineering delivering real-world solutions (not just POCs).
Strong hands-on skills with LLMs (OpenAI, Anthropic, Azure OpenAI) and agentic AI frameworks like LangGraph, LangChain, AutoGen, CrewAI.
Proficient in Python, FastAPI, asynchronous programming, RAG pipelines, vector DBs (FAISS, Pinecone, pgvector), cloud deployment (Azure/AWS/GCP).
Bachelor’s or Master’s degree in Computer Science, IT, or equivalent practical experience. Location: Bengaluru, Gurugram, or Mumbai.
Experienced in advanced AI system design including multi-agent architectures with observability and guardrails.
Proficient in enterprise integration technologies such as Graph API, Power Automate, Salesforce, and secure credential management.
Demonstrated ability to develop scalable backend services and manage LLM evaluations, prompt engineering, and human-in-the-loop workflows in production environments.