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Niche LLM/agent expertise and seniority reduce applicant pool despite metro location and general AI demand.
Role requires specialized LLM, agentic AI and graph expertise, limiting cross-industry interchangeability.
Explicit 9+ years and mandatory AI/LLM experience create stringent screening filters.
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Design and develop AI-first applications leveraging LLMs, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and agent-based architectures for intelligent automation.
Build and optimize autonomous and multi-agent AI workflows, integrating MCP-based tools with enterprise systems.
Collaborate with architects and cross-functional teams to deliver scalable, reliable, and secure AI-enabled cloud solutions while mentoring developers on AI engineering best practices.
Bachelor's degree in Engineering, Computer Science, or equivalent.
9+ years of professional software development experience with at least 3 years building AI/LLM and agentic systems.
Strong hands-on experience with LLMs (Azure OpenAI, OpenAI, Claude API, etc.), Retrieval-Augmented Generation, prompt engineering, embeddings, vector databases, and agent frameworks (Semantic Kernel, LangChain, LlamaIndex).
Experience with Azure cloud platform, AI ecosystem, relational databases, multi-threading, Git, Python AI/ML pipelines, and exposure to graph databases (Neo4j, Cosmos DB).
Engineer focused on building production-grade, scalable AI systems and intelligent workflows beyond prototypes.
Expertise in designing end-to-end agentic AI architectures including multi-agent orchestration and tool integration following protocols like MCP.
Balance of strong software engineering skills and deep AI/ML knowledge, capable of integrating AI innovations into enterprise-grade cloud environments.