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Tier-1 employer and metro location increase applicant volume, but specialized knowledge-graph and agentic AI focus limits competition.
GenAI and knowledge-graph skills are specialized yet transferable, yielding medium background sensitivity.
Multiple mandatory technical skills and senior title require narrow specialist experience, raising shortlisting strictness.
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Lead design and build of GenAI assistants and agentic AI solutions to optimize global supply chain operations, focusing on planning, sourcing, and delivery improvements.
Develop end-to-end large language model (LLM) applications incorporating RAG and Graph RAG pipelines, full-stack engineering (front-end and back-end), and deploy production-ready systems on AWS.
Define and implement reusable AI components, deploy scalable services from PoC to production with monitoring, SLIs/SLOs, and collaborate closely with product, engineering, and supply chain leadership to prioritize and scale AI-driven solutions.
Strong full-stack software engineering skills with front-end (React/Next.js, TypeScript) and back-end (Python/FastAPI or Node.js) production experience.
Hands-on experience building agentic AI solutions involving tool/function calling, planning/reasoning loops, and orchestration frameworks (e.g. LangGraph, Bedrock Agents).
Solid experience with GenAI/LLM tech including RAG/Graph RAG, embeddings, vector databases, retrieval, and ranking.
Proficiency deploying and operating applications on AWS services (Bedrock, SageMaker, Lambda, ECS/EKS, Open-Search).
Bachelor's degree in IT, Computer Science, Data Analytics, or related field.
Work Experience Required: Not explicitly mentioned in the JD
Experienced in translating complex supply chain challenges into scalable AI products leveraging agentic AI and knowledge graph integrations for reasoning and root-cause analysis.
Proven ability to lead cross-functional teams in agile environments building full-stack AI solutions from prototype to resilient, monitored production services on cloud.
Familiar with multi-agent AI architectures, orchestration frameworks, and the end-to-end LLM application lifecycle including evaluation and operationalization.