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Mid-level popular data role in a metro location increases candidate density despite GenAI specialization.
GenAI and production deployment skills are transferable, but enterprise integration knowledge adds domain specificity.
Requires specific GenAI, LangGraph, RAG and production deployment skills, but no explicit years requirement.
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Design, build, evaluate, and deploy Generative AI agent solutions and workflows using frameworks like LangGraph.
Translate business needs into clear GenAI use cases and architect agent workflows including tool calling, memory, and human escalation.
Develop modular, scalable Python code; optimize RAG knowledge stores; and collaborate with engineering teams for production deployment with monitoring and controls.
Experience building and deploying Generative AI agents or agentic workflows, preferably using LangGraph or similar frameworks.
Proficient in Python coding, focusing on modular, clean, production-ready code.
Ability to implement and optimize RAG knowledge stores and connect agents with enterprise data and APIs.
Work Experience Required: Not explicitly mentioned in the JD
Experienced in conceptualizing and architecting end-to-end Generative AI agent solutions aligned with business needs.
Comfortable working cross-functionally with data, platform, and software engineering teams for production-grade deployment.
Skilled in evaluating AI agents on metrics like reliability, hallucination risk, latency, cost, and user experience.