Lead Decision Intelligence Engineer; AI; NBA

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The Lead Decision Intelligence Engineer (AI) owns the application of Decision Intelligence and agentic AI across the NBA platform. This role analyzes and formalizes the business decisions that drive member engagement, translating stakeholder objectives, constraints, policies, and available data into structured decision models that can be evaluated, optimized, and automated. Working closely with business, product, and engineering teams, you identify where decisions should remain rule-based, where predictive models should be applied, and where agentic systems can create measurable value.

You then design and build production-grade decision intelligence capabilities that help teams create, understand, optimize, and govern member actions. Using Lang Graph, Lang Chain, Azure OpenAI, Azure AI Foundry, Databricks, and Humana's AI Gateway, you build agentic workflows that reason through decision processes, generate recommendations, explain tradeoffs, assist with action authoring, and continuously improve decision outcomes. This is a hands-on technical leadership role that combines decision science, AI engineering, and software architecture while leading a small team of engineers.

Decision intelligence modeling —

Decision decomposition —

Optimization strategy —

Agentic workflow delivery —

Action Library intelligence —

LLM and agent engineering —

Knowledge and retrieval systems —

Reinforcement learning integration —

Evaluation and experimentation —

AI governance and safety —

Team leadership —

Cross-functional partnership —

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 Lang Graph, Lang Chain, Auto Gen, 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.

Experience with Decision Intelligence methodologies, decision modeling notation, decision requirements analysis, influence diagrams, decision graphs, or business decision management frameworks.

Experience operationalizing reinforcement learning, contextual bandits, recommendation systems, or next‑best‑action optimization platforms.

Experience with Databricks, MLflow, Feature Store, Mosaic AI, or enterprise machine learning platforms.

Experience with Azure AI Search, vector databases, semantic retrieval systems, and enterprise knowledge architectures.

Experience with observability platforms such as Lang Smith, Open Telemetry, Prompt Flow, Azure Monitor, or equivalent AI monitoring solutions.

Experience integrating AI capabilities into enterprise software platforms and workflow‑driven applications.

Familiarity with Adobe Experience Platform (AEP), Salesforce, CRM platforms, healthcare engagement platforms, or marketing technology ecosystems.

Background in healthcare, insurance, or another highly regulated industry with auditability, explainability, and compliance…