Role: Lead AI Architect | Engineering AI & Agentic Systems: Engineering AI Program / Digital Engineering Systems
Location: Remote
Key Responsibilities
Engineering AI architecture and strategy
Own and evolve the engineering AI reference architecture, integrated with central IT AI services (e.g., enterprise AI gateway, model access, identity, agent registry, orchestration and shared observability) without duplicating them.
Design domain-specific capabilities, including engineering ontology, knowledge graphs where justified, semantic/context services, retrieval, reusable agent tools and engineering workflow patterns.
Make informed build-versus-buy and technical-pattern decisions, choosing among LLM agents, deterministic rules, geometric computation, simulation, surrogate models and hybrid approaches.
Engineering context and knowledge architecture
Define relationships and traceability among requirements, CAD models, parts, assemblies, BOMs, materials, revisions, simulations, test results and engineering documentation.
Architect grounded retrieval, engineering RAG/graph retrieval, metadata and provenance, revision-aware context, and fine-grained access controls.
Partner with OneData/Databricks and application owners on trusted engineering data pipelines and context services.
Agents, integrations and MCP engineering
Lead architecture and hands-on development of AI agents and multi-step engineering workflows, including tool calling, state, approvals, exception handling and auditable actions.
Design, build and sustain engineering MCP servers, API adapters and integration services for CAD, PLM, CAE and adjacent applications; assess vendor-managed versus custom capabilities.
Establish secure authentication/authorization, identity propagation, interface contracts, versioning, CI/CD, evaluation, monitoring and production support for agents and MCP services.
Vendor AI solution assessment and technical authority
Lead technical due diligence and architecture reviews for CoLab automated design review, Synera simulation workflows, PTC Creo/Windchill AI, Ansys AI, AWS consulting and future engineering AI partners.
Challenge vendors on underlying mechanisms (LLMs versus rules/algorithms), CAD geometry interpretation, simulation validity, system interfaces, data handling, security, accuracy, extensibility and operational support.
Define technical acceptance criteria, reference architectures, documentation, reusable assets, handover requirements and ownership boundaries to minimize avoidable vendor lock-in.
AI security and governance
Work with cybersecurity, AI governance and application teams on onboarding new vendor solutions or existing vendor AI solutions and secure integrations needed.
Delivery leadership and team development
Own solution architecture for engineering AI use cases such as Engineering Admin Assistant, automated design review, and simulation workflow automation, from feasibility and POC through production.
Translate engineering SME needs into technical designs and guide internal engineers and external partners through design reviews, code reviews and release readiness.
Hire, mentor and technically lead agent developers, engineering integration/MCP engineers, knowledge/data engineers and AI software engineers as the portfolio scales.
Required Qualifications
Bachelor’s or Master’s degree in computer science, software engineering, engineering, AI or related discipline typically 12+ years of relevant software/architecture experience.
Demonstrated hands-on delivery of production generative AI and agentic AI solutions, including LLM tool use, orchestration, retrieval, evaluation and runtime reliability.
Strong Python/backend development and enterprise API/integration experience; practical MCP/server-tool integration experience or equivalent expertise.
Meaningful engineering applications knowledge across at least two of CAD, PLM, CAE/simulation, engineering requirements or NPD workflows.
Experience with enterprise architecture, cloud, identity and access control, DevSecOps, deployment operations and technical risk assessment.
Ability to challenge vendors at implementation depth and lead technical teams across internal stakeholders and partners.
Preferred Qualifications
Experience with PTC Creo/Windchill, Ansys, CoLab, Synera or comparable industrial engineering ecosystems.
Knowledge of ontology design, knowledge graphs, GraphRAG, vector search, semantic data models and engineering data lineage.
Experience with LangGraph, Semantic Kernel, Amazon Bedrock or equivalent platforms Databricks/OneData integration; event-driven systems and cloud-native deployments.
Exposure to geometric feature analysis, deterministic engineering checks, solver automation, surrogate models, digital thread and automated design validation.
Experience designing technical standards and scaling reusable AI solution components across multiple business units.
First-Year Outcomes / Success Measures
Approved engineering AI reference architecture and explicit central-IT-versus-engineering ownership model.
Reusable secure engineering context, agent and MCP/API patterns adopted by multiple use cases.
Production-ready architecture and measurable quality controls for priority Assistant, CoLab and Synera integrations.
Engineering AI vendor-review standards, technical acceptance checklists and documented support/ownership handovers.
Sustainable internal development capability, engineering AI operations model and reduced dependence on single implementation partners.