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 βsafe failure modesβ: 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 CentralReachβs 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
