Lead Decision Intelligence Engineer (AI) - NBA

Humana; Boston (MA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Humana seeks a Lead Decision Intelligence Engineer to own decision intelligence across the NBA platform. You will translate stakeholder objectives into structured decision models, design agentic workflows, and build production-grade capabilities using LangGraph, LangChain, and Azure AI tools.

You will guide a small team of engineers, collaborate with business and product partners, and help govern decision outcomes with measurable value. Strong Python and enterprise AI experience are essential.

Qualifications

• 6+ years in software or AI engineering, with 1–2 years in tech leadership.
• Strong Python engineering experience delivering production AI systems.
• Hands-on experience building agentic applications with orchestration frameworks.

Responsibilities

• Model decision intelligence and decompose complex decision problems.
• Design and implement agentic workflows and production-grade services.
• Lead a small engineering team while remaining hands-on contributor.
• Collaborate with business, product, and engineering on governance and safety.

Skills

Python

Leadership

Communication

Problem solving

Tools

LangGraph

LangChain

Azure OpenAI

Azure AI Foundry

Databricks

Job description

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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 LangGraph, LangChain, 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.

Key Responsibilities

• 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 ---

Use your skills to make an impact

Required Qualifications

• 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.

Preferred Qualifications

• 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 LangSmith, OpenTelemetry, PromptFlow, 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 requirements.

Key Responsibilities

Microservices & Backend Engineering

• Architect, implement, and operate microservices that deliver:

• Action and variant metadata

• Context‑aware policy and eligibility evaluation

• Versioned, read‑optimized APIs for high‑performance runtime consumption

• Guarantee that services are:

• Highly available, with low latency

• Horizontally scalable for increased demand

• Backward compatible to support safe evolution and upgrades

• Apply industry best practices for API design, schema evolution, service isolation, and secure integration.

Database & Schema Design

• Design, deploy, and maintain resilient database schemas to support:

• Comprehensive action and variant catalogs

• Versioning, lifecycle management, and effective dating

• Rule bindings and complex metadata relationships

• Select and operate appropriate data stores (relational, document, key‑value) tailored to workload and scalability requirements.

• Implement and monitor:

• Schema migration and backward compatibility strategies

• Indexing and query optimization for performance

• Data integrity, consistency, and reliability

• Auditability and traceability for compliance and governance

Rules & Policy Engine Integration

• Integrate and manage enterprise‑grade rules engines to support:

• Eligibility, constraints, and business policies

• Suppression, cooldowns, exclusions, and other operational guardrails

• Policy‑driven allow/deny logic

• Work with technologies such as Drools (DRL/DMN), IBM ODM, DMN‑based services, OPA/Rego, or similar.

• Ensure rule execution is deterministic, versioned, stateless, and free from unintended side effects.

AI‑Assisted & Agentic Engineering

• Utilize AI‑powered and agentic tools to:

• Generate and refactor database schemas and service logic

• Streamline rule authoring, validation, and ongoing refactoring