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Medium competition due to senior ML title but niche GIS and agentic AI specialization.
High because specialized GIS, spatial AI, and agentic LLM expertise are required.
High due to mandatory GIS, agentic AI, AWS and LLM implementation expertise requirements.
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Design and build multi-agent AI systems using frameworks like LangChain, LangGraph, Agno, and Ollama.
Implement MCP (Model Context Protocol) server architecture with tool calling, resource management, and documentation exposure.
Fine-tune and operate large language models locally for domain-specific GIS and spatial AI problem solving, translating business needs into AI workflows integrated with enterprise systems.
Experience in GIS, Geospatial Data Science / Spatial AI, and Agentic AI solutions on AWS.
Hands-on experience designing and building multi-agent AI systems using frameworks such as LangChain or LangGraph.
Proficiency in model evaluation, feature engineering, handling class imbalance (e.g., SMOTE), and hyperparameter tuning for classification and regression models.
Work Experience Required: Not explicitly mentioned in the JD.
Senior engineering experience implementing agentic AI systems with autonomous reasoning, planning, human-in-the-loop validation, and explainable AI (XAI).
Strong background in integrating AI solutions within existing enterprise systems in a domain-specific geospatial context.
Operating style includes building advanced AI architectures and translating complex business problems into scalable AI workflows.