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Remote role and mid-level seniority increase applicant density, but niche agentic AI specialization limits it.
Highly domain-specific agentic AI, MCP, and LLM orchestration skills reduce cross-industry transferability.
Explicit 5–10 years requirement plus mandatory 2+ years in generative/agentic AI and many required tech skills.
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Design, build, and deploy production-grade autonomous and semi-autonomous AI agents that execute complex multi-step workflows integrating LLMs, APIs, enterprise applications, and developer tools.
Develop context engineering strategies, AI agent harnesses, and orchestration frameworks (e.g., LangGraph, LangChain) to manage agent state, permissions, retries, and validation loops to ensure reliability and scalability.
Lead integration of Model Context Protocol (MCP) servers and tools, implement secure tool-calling mechanisms, and develop comprehensive agent evaluation frameworks for business-impactful AI solutions in enterprise environments.
5–10 years of experience in Data Science, AI/ML, or Software Engineering, with at least 2 years hands-on in Generative and Agentic AI developing production-grade AI applications.
Strong proficiency in Python, API development (FastAPI or similar), context and harness engineering for AI agents, and experience with AI coding agents such as Claude Code or OpenAI Codex.
Experience in agent orchestration frameworks (LangGraph, LangChain, Semantic Kernel, AutoGen) and MCP (Model Context Protocol) integration with enterprise systems and tools.
Solid software engineering background including cloud platforms (AWS/Azure/GCP), CI/CD, Docker, databases, asynchronous processing, and production optimization for scalable AI deployments.
Technical leader skilled in architecting and scaling complex agentic AI workflows that move beyond prototypes to secure, production-ready solutions with measurable business impact.
Experienced in multi-disciplinary collaboration translating business/product requirements into integrated AI systems employing advanced context, tool orchestration, and automated evaluation.
Strong background combining deep AI/ML expertise with software engineering discipline and cloud-native production experience to optimize performance, cost, and reliability of AI agents.