AI Architect

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.