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Strong brand and metro location but niche generative/agentic AI skills reduce applicant density.
Highly specialized generative AI and agentic systems skills limit cross-industry transferability.
Explicit 5–7 years plus many mandatory GenAI, agentic, and tooling requirements increase filter strictness.
Design and implement scalable generative and agentic AI solutions integrated into enterprise Controls Technology platforms.
Architect advanced context engineering, Retrieval-Augmented Generation (RAG), knowledge graph, and multi-agent orchestration systems ensuring reliability, provenance, and operational efficiency.
Lead integration, deployment, observability, and compliance of AI/agentic systems in production environments while mentoring junior developers.
5–7 years of professional experience in AI or software development with significant generative and agentic AI expertise.
Bachelor's or master's degree in Computer Science, Data Science, AI, or related field.
Hands-on experience with generative AI concepts, prompt/context engineering, RAG systems, knowledge graphs, and multi-agent AI frameworks (Google ADK, LangGraph, etc.).
Proficiency in Python, containerization (Docker), orchestration (Kubernetes), cloud platforms (AWS or equivalent), and AI compliance/governance practices.
Experienced in architecting and deploying production-grade generative and agentic AI solutions in enterprise settings with cross-functional teams.
Strong technical leadership capability demonstrated via mentoring and delivering AI-driven business projects.
Proficient in advanced AI system design including multi-agent orchestration, knowledge graph pipelines, and stateful agent harnesses for scalable, safe AI operations.