Sr. Director, AI Architecture

It pairs hands on technical ownership with the second line leadership, operating plan and budget ownership, and governance responsibilities of the Sr. Director, Architecture (SPB1) standard, with five AI Engineers, one aligned to each business, as direct reports (growing to include team leads), and the mandate to grow the function as the portfolio expands. Lead and coordinate the technical and business discussions on future AI architectural direction across multiple teams and complex product lines, anchored to real propulsion and turbomachinery engineering problems.Analyze, design, and develop a roadmap and implementation plan for AI enabled engineering capabilities based on a current vs. future state, in a cohesive architecture viewpoint aligned to the digital thread.Review, analyze, and develop architecture at the domain level and across multiple teams, reference architectures for data pipelines, model and agent deployment, evaluation, and integration with PLM and existing simulation and analysis toolchains.Ground the reference architecture in the realities of aero engine, propulsion, and turbomachinery engineering, design, analysis (CFD, FEA, thermal and structural), test, manufacturing, and sustainment, defining how AI integrates with model based systems engineering, the digital thread, PLM, engine and rig test data, and the existing simulation toolchains.Participate in the enterprise architecture domain governance model, and define the AI governance, security, and export control posture (ITAR / export control, CMMC, RMF / ATO) so solutions are compliant by design across classified and unclassified environments.Contribute to the development of software, data, and technology platforms with reusable components across teams that can be orchestrated together, capturing reusable patterns, libraries, and reference implementations from each delivery.Lead the research and evaluation of emerging AI technology, industry, and market trends (LLM and retrieval augmented generation, vector databases, agentic orchestration) to inform development and operational support across multiple teams; run proof of concept builds to validate architectural and physical assumptions, including where physics informed or surrogate modeling of engine analysis (CFD/FEA/thermal) is appropriate versus where it is not, before committing the team.Set model and agent lifecycle practice: evaluation against engineering ground truth, versioning, monitoring, drift detection, retraining triggers, and rollback.Provide leadership, technology guidance, and mentorship throughout the domain; provide architecture direction to the embedded AI Engineers, review their designs, and remove technical blockers to keep delivery moving.Be a technical leader within the function and develop and coach the technical resources within it; serve as the single technical interface to enterprise platform, data, and security functions.Influence from a strategic and technical standpoint across the function and the business; present business and technical discipline solutions to senior leaders, communicate complex messages, and negotiate internally and with external partners, vendors, and customers to align on direction, tradeoffs, feasibility, and risk.Own and influence the budget and operating plan for the AI engineering function; provision and budget via capital and operating, and manage financials across programs and projects.Develop peer, cross functional, and cross business relationships to maximize best practice sharing and team effectiveness.Be responsible for management activities including recruiting, development, performance management, compensation, organization, and teaming.Lead the AI Engineering team, five AI Engineers, one aligned to each business, plus team leads as the function scales toward second line management, with the ability to attract, develop, and retain talent and to grow the team as the portfolio expands.Share technical, procedural, and business knowledge with others. Advanced DegreeTechnical ExpertiseAbility to consult with the business on the alignment of outcomes and desired AI technical solutions at an enterprise level.Ability to analyze, design, and develop an AI solution roadmap and implementation plan based on a current vs. future state of the business.Demonstrated architecture experience covering model training, deployment, and monitoring at enterprise scale, on a strong software engineering foundation: design patterns, APIs, integration approaches, cloud (AWS, Azure, or GCP), and containerization (Docker, Kubernetes).Hands on experience with LLM and retrieval augmented generation patterns, vector databases, and agentic orchestration frameworks.Working knowledge of configuration choices and related cost implications, compute, model hosting, and build vs. buy, and experience with complex solution configurations.Working knowledge of data engineering, data governance, and assessing data readiness for AI on messy, real engineering data.Experience sustaining operational stability through lifecycle phases (planning, implementation, steady state, decommissioning), including the model and agent (MLOps) lifecycle; ability to provision and budget via capital and operating.Experience delivering inside aerospace & defense or comparable regulated constraints: ITAR / export control, CMMC, RMF / ATO, and airgapped or impact level environments.Working fluency in the physics and engineering of aero engines and turbomachinery, aerodynamics, combustion, heat transfer, structures and rotordynamics, and materials, with the judgment to tell where a model is physically credible; familiarity with CFD, FEA, and thermal/structural simulation, engine and rig test data, surrogate and physics informed modeling, model based systems engineering, the digital thread, and PLM.Able to lead early stage interactions with domain engineering teams; guide them as they develop confidence and comfort with the approach and integrate it with their legacy tooling and investments.Leads others to find creative solutions within complex, interdependent engineering and production processes, drawing on multiple internal and external resources to evaluate conflicting information and arrive at decisions. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set. No individual has a vested right to any benefit under a Sponsor’s welfare benefit plan or program.

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