Director - AI Engineering

The Director – AI Engineering (Products & Governance) will play a pivotal role in engineering scalable, production grade AI products while embedding governance, risk, and responsible‑AI controls directly into the AI development lifecycle. This role is accountable for translating organizational, regulatory, and ethical AI policies into practical engineering standards, reference architectures, and automated guardrails across model development, deployment, and operations.

The role partners closely with business stakeholders, product leaders, department leaders, legal, risk, and compliance teams to enable rapid AI innovation without compromising trust, safety, or regulatory obligations. The Director is accountable for strengthening regional AI engineering and governance capabilities in service of Providence and other partnering health systems ensuring AI products are robust, auditable, explainable, and compliant by design.

 Strategic:

• AI Platform & Ecosystem Strategy
  Define the enterprise strategy for AI platforms, agentic systems, and reusable frameworks—balancing scalability, interoperability, cost, and long‑term flexibility across products and domains.
• Product‑Driven AI Governance Strategy
  Shape AI governance as an enabler of product velocity—embedding compliance, risk management, and ethical controls directly into architectures, SDLC, and operating models rather than as after‑the‑fact checks.
• Enterprise Model & Tooling Strategy (Build vs. Buy)
  Lead strategic evaluation of foundation models, orchestration frameworks, MCP ecosystems, and tools—making informed build‑vs‑buy decisions aligned to cost, latency, risk, vendor lock‑in, and regulatory exposure.
• AI Investment & Value Realization Frameworks
  Define and track AI‑for‑Engineering KPIs (cost, latency, throughput, reuse, quality, adoption, reliability) that connect technical choices to measurable business and product outcomes.
• Future‑Ready AI Architecture Vision
  Anticipate advances in multi‑modal AI, agentic reasoning, and distributed systems, translating emerging trends into a pragmatic enterprise roadmap rather than experimental sprawl.
• Information Architecture & Data Readiness Strategy
  Establish the strategy for AI‑ready information architectures and multi‑modal data ecosystems that support reasoning, grounding, memory, and context fusion at scale.
• AI Ops Strategy
  Responsible for establishing an AI Ops strategy across LLM and Agent Ops, ensuring continuous observability, evaluation, and feedback loops.

Leadership:

• Technical Leadership of Senior Experts
  Lead, mentor, and challenge highly experienced AI and systems engineers—shifting focus from individual implementation to architectural judgment, quality, and scale.
• Architecture Governance & Decision Stewardship
  Chair design reviews, own architectural decision records (ADRs), and resolve complex trade‑offs across performance, safety, cost, and maintainability at an enterprise level.
• Operating Model & AI‑DLC Ownership
  Establish and evolve the AI SDLC (AI‑DLC), ensuring consistent patterns for development, evaluation, deployment, monitoring, and iteration across all AI initiatives.
• Cross‑Functional Executive Partnership
  Partner with product, security, legal, compliance, and clinical/business leaders to align technical direction with enterprise priorities and risk posture.
• Standardization without Innovation Friction
  Drive adoption of playbooks, runbooks, SOPs, and reusable patterns while preserving room for innovation and domain‑specific differentiation.
• Culture of Engineering Excellence & Accountability
  Build a culture that values clarity, documentation, benchmarking, operational rigor, and learning—where engineers are accountable for production impact, not just clever designs.

Technical:

• Distributed Systems & AI Architecture Mastery
  Deep expertise in distributed system design, architecture patterns, reusable AI frameworks, and resilience strategies for large‑scale, production AI systems.
• Multi‑Model & Multi‑Agent Orchestration
  Ability to design and oversee complex agentic systems—including reasoning orchestration, coordination patterns, advanced agent architectures, and bounded autonomy.
• Context, Memory & Reasoning Engineering
  Mastery of context orchestration, memory management, multi‑source context fusion, and reusable “skills.md / workbooks” that standardize how intelligence is grounded and reused.
• Model Strategy & Selection Frameworks
  Expertise in enterprise‑grade model selection—balancing accuracy, explainability, latency, cost, data sensitivity, and governance requirements by use case.
• AI Quality, Benchmarking & Observability
  Define AI quality frameworks, benchmark systems, evaluation pipelines, and observability practices covering accuracy, drift, reasoning quality, cost efficiency, and reliability.
• Cost, Latency & Operational Optimization
  Deep understanding of cost and latency trade‑offs across models, agents, orchestration layers, and infrastructure—ensuring AI systems are economically viable at scale

• Bachelors/ Masters in STEM. Healthcare areas of specialization with ongoing engagement in emerging AI technologies, agentic systems, and applied research is preferred
• 15+ years of engineering experience, with 8–10+ years leading large‑scale platform or product engineering teams, including senior architects and domain experts delivering production‑grade systems.
• Proven track record building and scaling AI‑powered platforms or products in complex, distributed environments—moving solutions from experimentation to enterprise adoption, reuse, and sustained value.
• Deep experience designing and governing advanced AI systems, including multi‑model, multi‑agent architectures, orchestration frameworks, and reasoning systems, operating under clear performance, cost, and reliability constraints.
• Demonstrated leadership in AI governance and responsible AI, including defining frameworks for model lifecycle management, quality benchmarking, compliance, risk controls, auditability, and ethical AI practices, ideally in regulated or high‑trust environments.
• Strong background in enterprise architecture and distributed systems, with the ability to oversee decisions on scalability, latency, resilience, cost optimization, and technical debt across platforms and products.
• Experience driving standardized AI SDLC (AI‑DLC) practices—creating playbooks, runbooks, operating procedures, and architectural decision records that enable consistency, speed, and safety across teams.