Role: Lead AI Architect | Engineering AI & Agentic Systems
Engineering AI Program / Digital Engineering Systems
Location: Remote
Direct Client
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.
