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Niche agentic AI expertise reduces applicant density despite metro location and mid-seniority.
Highly domain-specific agentic AI and enterprise deployment experience limits cross-industry transferability.
Explicit 6–15 years plus mandatory ML/AI and agent-framework skills make filters strict.
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Own the end-to-end design, development, deployment, evaluation, and continuous improvement of production-grade Agentic AI solutions using LLM-powered multi-agent systems within enterprise client environments.
Integrate AI agents with enterprise applications, APIs, databases, and workflows, ensuring reliability, security, and measurable business impact in production.
Lead client engagement from problem discovery through production adoption, translating business needs into scalable, secure AI agentic solutions with ongoing monitoring and iteration.
6–15 years of experience in AI Engineering, Machine Learning, Software Engineering, Data Science, or related field with hands-on production AI deployment.
Strong Python development skills including building APIs and AI services (e.g., FastAPI).
Proven experience developing LLM-powered autonomous or semi-autonomous agents using frameworks like LangChain, LangGraph, or equivalents.
Experience with agent context engineering, agent harness design, agent evaluation frameworks, and integrating AI agents with enterprise tools and MCP.
Technical leader skilled in translating complex business problems into reliable, production-ready AI agentic solutions with measurable impact.
Experienced in client-facing roles, capable of collaborating across engineering, product, and business teams to drive AI adoption and continuous improvement.
Expertise in AI agent orchestration, context management, evaluation, and enterprise system integrations to build scalable and secure multi-agent workflows.