Lead AI Engineer - NBA

The Lead AI Engineer owns the design and delivery of the NBA platform's agentic AI capabilities. This is a hands‑on engineering role first - you are expected to be a significant individual contributor building production AI systems every sprint. You also lead a small team of engineers and contractors focused on bringing AI-powered workflows and intelligent analysis into the platform's user-facing experiences.

This role is forward‑looking: you are building the foundation for how AI augments and automates decision-making across the platform.

• Agentic AI delivery - design, build, and own AI agent workflows that surface intelligent analysis, recommendations, and automation across the NBA platform's user interfaces and internal tooling

• Hands‑on development - actively write and ship production code alongside your team; this is not a supervisory role

• AI workflow design - architect multi‑step agentic pipelines that integrate with the platform's data layer, decisioning outputs, and activation channels to deliver meaningful AI‑driven experiences

• LLM integration - own the integration of large language models (Claude, GPT, or equivalents) into platform workflows, including prompt engineering, tool use, structured output handling, and context management

• User‑facing AI experiences - partner with frontend and product teams to bring AI-powered analysis and recommendations into the Command and Control UI and other operator‑facing surfaces

• Evaluation and reliability - build evaluation frameworks to measure AI output quality; ensure agentic workflows are reliable, auditable, and safe in a regulated healthcare environment

• Contractor management - manage AI engineering contractors within your pod; set expectations, review work, and hold the team to quality standards

• AI productivity tooling - model best practices for AI‑assisted development (Claude, Copilot, etc.) across the broader engineering organization

• 6+ years of software or ML engineering experience with at least 1-2 years focused on LLM integration, agentic AI, or AI product engineering

• Hands‑on experience building production agentic systems - multi‑step pipelines, tool orchestration, structured output handling, and multi‑turn conversation management

• Experience with RAG patterns and vector search - you have built retrieval‑augmented systems in production, not just read about them

• Strong Python engineering skills; comfortable building and shipping production‑grade AI systems, not just prototypes

• Experience integrating LLM APIs (Anthropic, OpenAI, or equivalents) into real applications

• Proven ability to lead a small team or pod through delivery and manage contractor resources

• Strong judgment on when AI is the right tool and when it is not - especially in a regulated, member‑facing context

• Clear communicator - you can explain an agentic architecture to an engineer and a product owner without losing either of them

• Deep fluency with AI productivity tools (Claude, Copilot, or similar) - this is table stakes for this role

• Experience orchestrating multi‑agent systems - coordinating multiple AI agents across complex, long‑running workflows

• Experience integrating agentic AI into bespoke enterprise software and connecting to external systems like Salesforce, Adobe Experience Platform, claims platforms, or similar

• Experience building AI‑powered analytics or insight surfaces in a business intelligence or operational UI context

• Background in healthcare, insurance, or other regulated industries where AI outputs carry compliance risk

• Experience with AI evaluation frameworks and techniques for measuring output quality and detecting drift

• Familiarity with…