Description:
Role Summary

What You’ll Do- Implement advanced RAG (Retrieval-Augmented Generation) techniques — hybrid search, re-ranking, contextual chunking, and long-context strategies — and agentic orchestration patterns (ReAct, plan-and-execute, multi-agent).
Build and integrate APIs(Application Programming Interface) and enterprise connectors, including MCP-based integrations, to connect AI (Artificial intelligence) capabilities with enterprise systems and data sources.
Deploy, configure, and operate AI (Artificial intelligence) workloads on the cloud, owning solutions end-to-end from prototype to production.
Design AI (Artificial intelligence) solutions that scale — accounting for performance, throughput, latency, cost, and reliability as usage grows from POC (Proof of Concept) to enterprise scale.
Rapidly prototype: take a loosely defined idea or business problem and produce a working proof-of-concept quickly, then iterate based on feedback.Required Qualifications
Solid hands-on software development experience in Python,



with a solid engineering foundation (you can design, write, test, and debug production code).
Hands-on experience with LangChain, LangGraph, and/or AutoGen, and with agentic orchestration patterns for building multi-agent or agentic AI (Artificial intelligence) systems.
Proven experience building RAG (Retrieval-Augmented Generation) systems, including advanced techniques such as hybrid search, re-ranking, contextual chunking, and long-context strategies.
Experience designing and consuming APIs(Application Programming Interface) and integrations; familiarity with MCP (Model Context Protocol) as an emerging integration pattern.
Solid, hands-on command of cloud concepts on at least one major provider (Azure preferred; AWS or GCP considered) — able to independently provision, configure, develop against, deploy, and operate AI (Artificial intelligence) workloads and cloud resources. Hands-on experience with Azure AI (Artificia

📌 Artificial Intelligence Developer Thane
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📍 Thane