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Metro location and in-demand AI skills with a recognizable employer create moderate applicant density.
Core ML/LLM skills transfer across industries but require LLM and vector DB specialization.
Multiple mandatory LLM frameworks, vector DB, prompt engineering and production AI requirements raise filtering rigidity.
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Develop and implement AI agent workflows and retrieval augmented generation (RAG) pipelines for enterprise AI systems using modern AI/ML and LLM frameworks.
Build production-ready AI components focusing on data discovery, enrichment, classification, contextual reasoning, and enterprise knowledge discovery.
Support AI evaluation routines including quality checks, hallucination detection, regression testing, and integration with LLM APIs and open source models.
Strong hands-on programming experience in Python with AI agent development.
Practical experience with LLMs, RAG, vector databases, embedding models, and prompt engineering.
Experience working with one or more AI/LLM frameworks like LangChain, LangGraph, LlamaIndex, AutoGen, or equivalent.
Familiarity with SQL, structured data concepts, REST APIs, microservices, Git workflows, unit testing, and modern software engineering practices.
Engineer experienced in building enterprise-level, production-ready AI/ML and agentic AI systems rather than prototypes or isolated demos.
Proficient with AI workflow orchestration frameworks and vector database technologies, demonstrating integration and tuning skills.
Comfortable working in collaborative engineering teams interfacing with architects, data engineers, and platform engineers to deliver scalable AI solutions.