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Niche agentic AI skills, small-brand company, and non-metro location create stacked competition amplifiers.
Specialized agentic AI, LangGraph, and context-engineering expertise limits transferability across industries.
Many mandatory, specialized technical skills and deep ML/agent engineering requirements impose strict shortlisting filters.
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Design and implement multi-agent AI workflows focusing on stateful, resumable processes with error handling and human-in-the-loop checkpoints.
Engineer advanced context management systems including layered context, filters, and token-budgeted prompt compaction to ensure precise agent inputs.
Integrate large language models and retrieval systems, build evaluation/error-analysis loops, and collaborate on deployment and durable execution of production-grade agentic AI systems.
Strong proficiency in Python 3.10+ with experience in asynchronous programming and typing.
Solid foundation in computer science fundamentals, specifically data structures, algorithms, and complexity analysis.
Experience designing and implementing multi-agent orchestration with LangGraph or equivalent frameworks; strong context engineering skills mandatory.
Bachelor's or Master's degree in Computer Science or related field from a reputable institution; work experience required: Not explicitly mentioned in the JD.
Experienced in designing and operating complex multi-agent AI systems with strong problem-solving and debugging abilities demonstrated by competitive programming or similar.
Comfortable working hands-on with the latest agentic AI stacks such as LangGraph, MCP, and multi-agent orchestration frameworks.
Able to design production-grade, maintainable AI workflows with rigorous evaluation, verification, and human-in-the-loop capabilities, collaborating across infrastructure and platform teams.