Lead AI Decision Intelligence Engineer

Humana Inc

Lincoln (NE)

On-site

USD 140,000 - 190,000

Full time

9 days ago

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Job summary

Humana Inc. is seeking a Lead Decision Intelligence Engineer to own decision intelligence and agentic AI across the NBA platform. The role analyzes business decisions, models constraints and policies, and translates data into production decision models.

You will design agentic workflows, integrate with Humana's AI Gateway, and guide a small engineering team. You will work with LangGraph, LangChain, and enterprise AI platforms to optimize member engagement, governance, and outcomes, while

Qualifications

Bachelor's degree in computer science or related field.
6+ years of software/ML/decision intelligence experience, including 1–2 years in technical leadership.
Strong Python experience for production AI systems.
Hands-on experience with agentic applications using LangGraph, LangChain, AutoGen, CrewAI or similar.
Experience integrating Azure OpenAI, Azure AI Foundry, Vertex AI, Anthropic, OpenAI or comparable platforms.
Understanding of decision intelligence concepts: modeling, optimization, automation, and outcome measurement.
Experience with LLM patterns: tool calling, structured outputs, and retrieval‑augmented generation.
Ability to map business processes into formal decision frameworks and communicate with stakeholders.
Proven ability to lead a small engineering team while remaining hands‑on.
Strong communication skills for senior leadership.

Responsibilities

Decision intelligence modeling and formalizing business decision processes.
Decompose complex processes into decision graphs and optimization opportunities.
Determine where rules, ML models, or agentic systems add value.
Design and implement production agent workflows using LangGraph/LangChain.
Develop AI‑powered capabilities in the Action Library to assist user actions.
Lead integration with enterprise AI platforms and Humana's AI Gateway.
Design retrieval architectures using vector search and knowledge sources.
Collaborate with data science to operationalize reinforcement learning and optimization.
Build evaluation frameworks for AI systems and governance mechanisms.
Lead engineering standards and cross‑team collaboration.