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Specialized LLM skills plus strong employer and metro location create moderate applicant competition.
Role requires niche LLM/agent engineering and enterprise RAG experience, limiting cross-industry transferability.
Explicit 7–10 years plus mandatory LLM, LangChain/LangGraph, PyTorch requirements make filters strict.
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Design and implement stateful multi-agent graphs using LangGraph to automate complex operational workflows.
Construct and manage advanced Retrieval-Augmented Generation (RAG) pipelines with hybrid retrieval, re-ranking, and context selection.
Build reliable API integrations for autonomous agents to query databases and process unstructured documents with focus on agent state persistence and efficient execution.
7 – 10 years software development experience, including at least 3 years on LLM application engineering, RAG, and agent frameworks.
Advanced mastery in Python, PyTorch/Transformers, LangChain, and LangGraph with state machine and parallel execution expertise.
Experience building enterprise RAG pipelines over complex unstructured documents such as PDFs, tables, financial filings.
Strong knowledge of async Python (asyncio), REST API design, microservices, and Git workflows.
Senior engineer with deep domain expertise in LLM frameworks and retrieval-augmented generation pipelines for unstructured financial or healthcare data.
Strong software engineering discipline with experience in production-grade Python code, testing, logging, and scalable system design.
Ability to mentor mid-level and junior developers in multi-agent engineering patterns and hybrid workflow automation.