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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 - Analyze and formally model business decision processes, including objectives, constraints, policies, decision points, outcomes, dependencies, and feedback loops that govern member engagement.
-Decision decomposition - Break complex business processes into decision graphs, decision services, decision hierarchies, and optimization opportunities that can be measured, automated, and improved.
-Optimization strategy - Determine where rules, predictive models, reinforcement learning, optimization techniques, or agentic systems create the highest business value and operational impact.
-Agentic workflow delivery - Design and implement production agent workflows using LangGraph and LangChain, including multi-agent collaboration, tool usage, workflow memory, planning, reasoning, and human-in-the-loop approval patterns.
-Action Library intelligence - Build AI-powered capabilities embedded directly into the Action Library that assist users in creating, refining, validating, governing, and optimizing member actions.
-LLM and agent engineering - Own integration with Azure OpenAI and other enterprise models through Humana's AI Gateway, including prompt engineering, structured outputs, retrieval patterns, tool calling, function execution, and workflow orchestration.
-Knowledge and retrieval systems - Design retrieval-augmented architectures using vector search, semantic retrieval, knowledge grounding, and enterprise content sources to provide reliable decision context.
-Reinforcement learning integration - Partner with data science teams to operationalize reinforcement learning and decision optimization models within NBA workflows, ensuring recommendations can be deployed and governed at scale.
-Evaluation and experimentation - Build rigorous evaluation frameworks that measure recommendation quality, decision quality, agent effectiveness, user adoption, business outcomes, and operational performance.
-AI governance and safety - Implement guardrails, observability, traceability, policy controls, human review mechanisms, and auditability requirements appropriate for a healthcare environment.
-Team leadership - Lead and mentor AI engineers, establish engineering standards, conduct design reviews, and drive execution across the Decision Intelligence workstream.
-Cross-functional partnership - Work closely with product, business, decision science, data science, and engineering teams to convert complex decision processes into production AI capabilities.
Use your skills to make an impact
Required 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.
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
