AI Architect

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.