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Strong brand, mid-level role, metro location, but niche LangGraph requirement limits pool.
Highly domain-specific LLM, LangGraph and AgentCore expertise reduces cross-industry transferability.
Explicit 3–5 years and many mandatory GenAI, LangGraph, RAG, and vector DB requirements.
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Design, build, and own intelligent AI automation solutions using LangGraph agentic workflows and AWS AgentCore, focused on production-grade delivery.
Develop end-to-end custom RAG pipelines including chunking, embeddings, vector retrieval, and accuracy evaluation.
Maintain production components including monitoring, debugging agent failures, optimization, and mentoring junior developers.
3-5 years total work experience with 1-2 years in Generative AI/LLM and 6-12 months hands-on LangGraph experience.
Strong proficiency in LangGraph including state graphs, conditional routing, checkpointing (non-negotiable).
Production-level Python skills including FastAPI, Pydantic, async/await, and testing.
Experience with AWS AgentCore or strong LangGraph expertise with willingness to ramp; also practical experience with vector databases and RAG architectures.
Deep hands-on expertise with AI agentic frameworks (LangGraph, AWS AgentCore) and development of robust AI automation in production environments.
Familiarity with multi-model AI providers and enterprise integrations like SAP HANA and Salesforce connectors.
Comfortable balancing pro-code and low-code AI solutions and leading rapid POCs to scalable implementations with mentoring responsibility.