Lead AI Decision Intelligence Engineer

Humana Inc

Bismarck (ND)

Hybrid

USD 129,000 - 178,000

Full time

7 days ago

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Job summary

Humana Inc. is seeking a Lead Decision Intelligence Engineer to shape production decision intelligence across our NBA platform.

You will formalize business decisions, design agentic workflows, and lead a small team of engineers to deliver scalable AI solutions using LangGraph, LangChain, and Azure AI Foundry. The role requires 6+ years in software/AI engineering with strong Python, and hands-on experience integrating enterprise AI platforms.

Qualifications

Bachelor's degree in computer science or related field.
6+ years of software engineering, machine learning engineering, AI engineering, or decision intelligence experience, including at least 1–2 years in a technical leadership capacity.
Strong Python engineering experience building and operating production AI systems.
Hands‑on experience building agentic applications using LangGraph, LangChain, AutoGen, CrewAI, or similar orchestration frameworks.
Experience integrating Azure OpenAI, Azure AI Foundry, Vertex AI, Anthropic, OpenAI, or comparable enterprise AI platforms.
Strong understanding of Decision Intelligence concepts, including decision modeling, optimization, decision automation, objectives, constraints, and outcome measurement.
Experience implementing LLM application patterns including tool calling, structured outputs, retrieval‑augmented generation (RAG), memory management, and workflow orchestration.
Experience building evaluation frameworks for AI systems, including automated evaluation, human review, performance measurement, and experimentation.
Ability to map business processes into formal decision frameworks and communicate those models to both technical and non‑technical stakeholders.
Demonstrated ability to lead a small engineering team while remaining a hands‑on contributor.
Strong communication skills with the ability to explain complex AI and decision architectures to senior leadership.

Responsibilities

Decision intelligence modeling — design and formalize decision models to drive outcomes.
Decision decomposition — break down complex decisions into actionable components.
Optimization strategy — develop approaches to improve performance and value.
Agentic workflow delivery — implement end‑to‑end agentic pipelines.
Action Library intelligence — organize and govern action libraries and metadata.
LLM and agent engineering — lead implementation of LLM apps and agentic systems.
Knowledge and retrieval systems — build systems for knowledge extraction and retrieval.
Reinforcement learning integration — integrate RL approaches where appropriate.
Evaluation and experimentation — establish measurement and validation frameworks.
AI governance and safety — ensure governance, safety, and compliance in AI solutions.
Team leadership — guide a small engineering team and collaborate with stakeholders.
Cross‑functional partnership — work with product, platform, and data teams to align architecture.