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
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
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
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
Requirements
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