Director, AI Product Development

CentralReach’s AI team operates as an AI Foundry: a cross-functional group that rapidly builds, validates, and scales AI-enabled product capabilities. The Director, AI Product Development is the senior-most engineer on the AI team and sets the technical bar for how AI-powered product experiences are designed, built, evaluated, and operated. 

This role is hands-on and deeply engaged across the early stages of development of AI applications, from early prototypes and pilots through production hardening and scale. They partner closely with AI product builders, Product organization leaders and DevOps to translate customer problems into reliable AI features that integrate seamlessly with CentralReach’s core workflows. They also establish engineering standards for AI application development: evaluation and quality thresholds, observability, guardrails and performance management.

Key Accountabilities: 

 Technical Leadership & Engineering Excellence 

• Serve as the technical lead for AI application development within the AI Foundry, setting standards for code quality, architecture, and delivery 

• Lead by doing: design and implement core AI application components, critical services, and integration layers 

• Mentor AI engineers; raise the bar on engineering rigor and AI-specific best practices 

• Establish quality thresholds and release criteria (accuracy, latency, reliability, cost, and user trust) 
• Design safeguards and “safe failure modes”: fallback behaviors, confidence thresholds, user controls, content filtering, and transparency patterns 

AI Application Development (Hands-On) 

• Build AI-powered product capabilities end-to-end (service + workflow integration + instrumentation), including LLM-enabled workflows, RAG, summarization, classification, and automation patterns 

• Build and maintain shared libraries/components for AI application development (prompt/tooling patterns, service templates, evaluation utilities, safety layers) 

• Own technical readiness for production: reliability, observability, performance tuning, and incident response preparedness 
• Collaborate with platform Engineering and DevOps to ensure CI/CD and environment consistency, scaling strategies, cost controls for inference and secrets management and secure data handling 

Cross-Functional Delivery & Integration 

• Partner tightly with AI product builders and workflow Product owners to translate validated prototypes into production implementations 
• Collaborate with core engineering teams to integrate AI capabilities into CentralReach’s main platforms 

Strategic Direction & Innovation 

• Identify and prioritize foundational investments that increase delivery velocity and reduce long-term maintenance: reusable components, platform primitives, and standardized patterns 

• Evaluate build vs. buy decisions for AI tooling and recommend approaches aligned to CR constraints 
• Stay current with AI application engineering practices and help translate emerging techniques into safe, valuable product capabilities 

Desired Skills and Experience: 

•  Bachelor's degree or equivalent work experience

• 10+ years of professional software engineering experience, with principal-level scope and demonstrated technical leadership 

• Strong experience building and operating production distributed systems and backend services 

• Demonstrated hands-on experience delivering AI/ML-powered product features (LLMs and/or traditional ML), including evaluation and monitoring 

• Experience with retrieval systems and search relevance (RAG, embeddings, indexing, ranking, evaluation) 

• Strong system design skills: APIs, data flows, integration patterns, performance and reliability tradeoffs 

• Experience with observability and operational excellence (logging, metrics, tracing, alerting, incident response) 

• Ability to communicate technical concepts clearly to product, design, and executive stakeholders 

• Experience in a healthcare SaaS environment 

• Familiarity with multi-tenant architectures and enterprise access control models 

• Experience building internal platforms/tooling that improve developer experience and standardize best practices

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