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
- Strong 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.
- Strong, 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 (Artificial intelligence) Foundry (or an equivalent managed AI (Artificial intelligence) platform).
- Understanding of how to design and scale AI (Artificial intelligence) solutions for production — performance, throughput, latency, cost optimization, and reliability — including patterns such as caching, asynchronous processing, load handling, and horizontal scaling.
- Demonstrated ability to rapidly prototype — translating an idea into a functional proof-of-concept quickly using AI-assisted (Artificial intelligence) development and fast iteration.

Preferred Qualifications

- Experience with AI (Artificial intelligence) evaluation and observability tooling (e.g., ragas, LangSmith, Promptflow evals, Azure Monitor).
- Familiarity with AI (Artificial intelligence) safety, responsible AI principles, and enterprise guardrail patterns (content filtering, grounding checks).
- Working knowledge of low-code AI (Artificial intelligence) platforms (Microsoft Copilot Studio, Power Platform) or agent-builder platforms (Lyzr, Moveworks) for rapid delivery where they are the right fit.
- Experience integrating with Microsoft 365 and Dataverse, or an equivalent enterprise ecosystem.

What Makes You a Fit

You are first and foremost a solid engineer who has moved into AI (Artificial intelligence) — comfortable living in code, reasoning about architecture, and owning a solution from prototype to production. You reach for low-code tools when they are genuinely the fastest path, but your default is to build in code, and you would rather ship a rough proof-of-concept today than a perfect specification next month.

The Application Process:

- Stage 2: Pre-recorded video interview
- Stage 3: Live video or in person interview with hiring manager and team
- Stage 4: Offer and onboarding

Qualifications :

- Bachelor or Masters degree in Computer Science, Information Technology, Business Administration, or a related field.

📌 Artificial Intelligence Developer (Pro Code) (Thane)
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