Director, AI Engineering

About the role

McKinstry is innovating the waste and climate harm out of the built environment and creating lasting impact. We’re adding a Director, AI Engineering to our team in Seattle, WA. The Director, AI Engineering owns the development of the company’s core AI technology and the platform it runs on, the enterprise engineering function, the technical strategy and reference architecture, and the production operations that keep released systems trusted.

The role turns approved, requirements-backed use cases into secure, production-grade systems, agents, APIs, and integrations for every spoke, built once to a shared standard. Partners closely with enterprise AI strategy and leadership on use-case selection, prioritized roadmaps, enterprise architecture, to ensure the right systems and technical foundation.

Responsibilities

• Own the AI technical strategy and reference architecture. Sets how AI systems are engineered: the reference architecture, integration patterns, agent platform, and build-versus-reuse calls that keep the portfolio coherent, built on McKinstry’s Filesystem-Driven Agent Architecture.
• Own the AI platform and the agent harness. Builds and operates McKinstry’s core AI platform: the agent harness that turns a model into a governed capability (provider gateway, routing, context, memory, tool execution, output validation, and instrumentation), implemented on the Filesystem-Driven Agent Architecture.
• Own the model-agnostic strategy and lead the build, buy, or host decision-making process. Keeps McKinstry portable across model providers through a provider-gateway layer, so changing a model or vendor is a configuration change, not a rebuild.
• Own the enterprise AI engineering function and standards. Runs the shared production build capability that serves every spoke, and sets the engineering standards, code-quality bar, and agent build-and-evaluation methodology.
• Own the AI marketplace. Owns the build and operation of the enterprise agent marketplace (McKinstry Labs): the catalog of approved, tested, reusable agents, skills, and tools that divisions discover and build on.
• Chair the architecture and production-readiness reviews. Owns the technical gate: nothing reaches production without passing it.
• Engineer approved work into shipped systems. Takes the prioritized scope set by the domain directors and their product managers and engineers it into production: design, build, test, deploy, and the move from prototype to production-grade system.
• Build and lead the engineering team. Recruits, develops, and directs the AI, ML, software, and platform management and engineers; allocates capacity against agreed priorities.
• Own production operations and the agent lifecycle. Runs deployment, monitoring, observability, LLMOps, versioning, and lifecycle management in production, including the evaluation harness and release gates that keep systems accurate and safe.
• Own the AI technology radar. Runs structured evaluations of emerging models, tools, and platforms and sets the program’s default technical position.
• Engineer security and responsible-AI controls into the build. Works with Information Security to build data protection, access control, model-access governance, prompt-injection defense, and output validation into the system.
• Build on the enterprise data foundation, not around it. Works with the Enterprise Data & AI Architect so every system reads and writes against the Four IDs and the MDM spine, and integrates into McKinstry systems such as Procore, Dynamics, and Fabric.

Requirements

• 15+ years in software engineering, with hands-on experience shipping and operating production systems, and enough recent depth to set the technical bar, not only manage to it.
• Demonstrated experience building and operating AI or ML applications in production (LLM applications, agents, RAG, evaluation, tool and function calling, multi-agent orchestration) as the person accountable for whether the system worked.
• Platform depth: has built or operated an agent harness or runtime and a provider-abstraction layer, with sound judgment on managed, self-hosted, and open-source model tradeoffs across cost, latency, and data sovereignty.
• Experience managing a team of 5+ direct reports, or 10+ including matrixed contributors, owning hiring, budget, and a delivery culture that balances exploration with production commitments.
• Architecture judgment: can set a reference architecture and integration patterns for a portfolio of systems and hold teams and vendors to them, while building against an enterprise data layer owned elsewhere.
• Fluent in modern cloud and AI delivery engineering (for example Azure, APIs, CI/CD, LLMOps, observability), able to integrate against enterprise systems and a master-data layer rather than building disconnected stores.
• Can translate product requirements and data constraints into a build plan engineers execute, and validate the result with end users.
• Security-by-design discipline: builds access control, data residency, model-access governance, prompt-injection defense, and output validation into the system rather than bolting them on later.
• Strong written and verbal communication: can write a technical design an engineer builds from, and report engineering status and risk to executives without rework.
• Comfortable owning the build, the platform, the technical strategy, and the solution architecture without owning enterprise strategy or product, engineering the right thing on a data spine owned elsewhere.
• Bachelor’s degree in computer science, engineering, or a related field; relevant experience may be substituted. Cloud or security engineering credentials preferred.

Benefits

• Competitive pay
• 401(k) with employer match and profit-sharing plan
• Paid time off and holidays
• Comprehensive medical, prescription, dental, and vision with low or zero deductible options and low out of pocket maximums
• Family formation benefits, including adoption and IVF assistance
• Up to 16 weeks paid parental leave
• Transgender inclusive benefits
• Commuter benefits
• Pet insurance
• “Building Good” paid community service time
• Learning and advancement opportunities via McKinstry University
• McKinstry Moves onsite gyms or reimbursement for remote workers

Pay

The pay range for this position is $165,800 - $252,100 per year; however, base pay offered may vary depending on job-related knowledge, skills, and experience. A bonus may be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits.