Role Summary

We are looking for a pro-code AI (Artificial intelligence) Developer who builds production-grade AI (Artificial intelligence) systems in code. You will design and build AI (Artificial intelligence) agents, RAG (Retrieval-Augmented Generation) pipelines, and AI-powered (Artificial intelligence) applications primarily in Python orchestrating LLMs (Large Language Models.)with frameworks such as LangChain, LangGraph, and AutoGen, integrating them into enterprise systems via APIs(Application Programming Interface) and MCP(Model Context Protocol), and deploying them on the cloud (including Azure AI (Artificial intelligence) Foundry). The ideal candidate has a strong software-engineering foundation, a robust bias to build, and can move from a loosely defined idea to a working proof-of-concept in days, not weeks.

What Youll Do

- Design and build AI (Artificial intelligence) agents, RAG (Retrieval-Augmented Generation) pipelines, and AI-powered (Artificial intelligence) applications in code (primarily Python), orchestrating LLMs(Large Language Models.) with frameworks such as LangChain, LangGraph, and AutoGen.
- 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.
- Apply sound engineering judgment — error handling, evaluation, observability, security, and access considerations — across the solutions you build.

Experience: 5 to 8 Years

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 strong 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.

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