Principal Embedded Engineer, AI Solutions

Job ID26WD101876Job Requisition ID Principal Embedded Engineer, AI SolutionsPosition OverviewAutodesk Technical Advisory is seeking a highly technical and customer-focused Senior Principal Embedded Engineer, AI Solutions to join our new Embedded Engineering (forward-deployed engineering) team. You will work directly alongside strategic Autodesk customers to discover high-value workflows, model the data those workflows depend on, and build the AI systems for their Design & Make workflows to enable customer's measurable business outcomes.You will move fluidly from understanding a customer's workflow and architecture to writing code. You will spend your weeks writing production code to build agents and orchestration, retrieval and graph services, connectors and APIs - inside customer environments, alongside customer engineers. What you ship is concrete: AI agents, MCP servers and tools, domain and semantic models, knowledge-graph extensions, retrieval and context-resolution pipelines, integrations into ERP, PLM, BIM, enterprise data platforms, and eval harnesses to move new capabilities into production.Unlike traditional project delivery, Embedded Engineering works deeply inside customer environments, connecting Autodesk technology and data with the rest of the enterprise. Insights developed through customer work will be translated into reusable agents, connectors, reference architectures, engineering patterns, and direct product feedback that shapes Autodesk's platform roadmap.The successful candidate combines strong software and AI engineering skills with curiosity, customer empathy, business judgment, and the ability to operate independently when the problem is not yet well defined. This role is engagement-driven with travel up to 75% during active engagements, including extended periods working onsite with customers.We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed below.ResponsibilitiesDiscover Workflows and Own the OutcomePartner with customer executives, business leaders, and technical teams to translate transformation objectives, operational KPIs, sustainability commitments, and digital initiatives into measurable, AI-enabled workflow outcomesLead co-innovation with customers to find high-value workflows, decision bottlenecks, data requirements, and integration opportunities, turning ambiguous business challenges into clear technical directionsAnalyze end-to-end workflows across Autodesk and non-Autodesk systems to pinpoint friction, process inefficiency, data gaps, and the places where AI and agents genuinely change the economicsOwn technical execution end-to-end - rapid prototype through architecture, build, integration, testing, governance, production deployment guidance, and adoption - with accountability for customer impactSequence the work and decide what not to build. Make explicit trade-offs between scope, speed, and durability, and defend them to both customer and Autodesk stakeholdersMake Data and Context AI-ReadyAssess customer data readiness - fragmented sources, ownership, quality, lineage, metadata, governance, permissioning, and integration constraints - required to support trusted AI and agentic workflowsStructure and connect design intent, project, product, and asset data, along with requirements, supporting evidence, approvals, decisions, and dependencies, into trusted AI-ready data and context models that enable reasoning, retrieval, and agentic actionDesign ontologies, semantic layers, and knowledge graphs that business users recognize and agents can act on, including entity resolution across source systems, schema mapping, and permission models that survive contact with enterprise realityBuild and Ship Production AIDesign and build production-grade AI-enabled workflows, agents, applications, and integrations that connect Autodesk products and platform capabilities with customers' broader enterprise technology environmentsBuild the retrieval and context layer these systems depend on - hybrid search, graph-grounded retrieval, tool interfaces, and MCP servers that expose governed enterprise context to agents safelyMake quality measurable. Build evaluation harnesses, golden datasets, guardrails, tracing, and observability so that agent behavior is testable before launch and monitored after it, and so that quality regressions and drift are caught in production rather than reported by usersApply strong engineering practices across architecture, code quality, security, permissions, reliability, observability, testing, performance, and maintainability, adapting quickly to complex and constrained customer environmentsDefine the operating model for what you build - runbooks, ownership, escalation, and cost - and hand it over cleanly to customer teams or Autodesk services so value persists after the engagement endsFeed the Product and Build Reusable IPAct as a frontline product discovery mechanism, translating customer learning into prioritized product requirements, platform capability gaps, roadmap recommendations, and opportunities for feature or API accelerationConvert customer-specific learning into reusable Autodesk value - reference architectures, agent and MCP patterns, connectors, ontology templates, graph models, reusable code, evals, and technical playbooks - that reduces the cost of every engagement that followsBuild an AI-Native PracticeDesign, build, and operationalize the agents, workflows, and automations that make the Embedded Engineering organization itself faster and more effective, measuring success by both customer impact and scalable internal leverage. Continuously look for manual work to replace with agentic systems and reusable internal toolingStay at the frontier. Maintain working knowledge of new model capabilities, agent implementation patterns, and AI product development stacks, and bring what's genuinely useful into practiceServe as a trusted technical advisor across the boundary - partnering with customer engineers, architects, and business leaders, and with Autodesk Sales, Customer Success, Technical Advisory, Product, and Platform teams - to unblock delivery and help teams navigate ambiguity, trade-offs, risk, and changing requirementsMinimum Qualifications6+ years building and operating production software, including 2+ in a customer-facing or embedded role (forward-deployed or founding engineer). CS/Engineering degree or equivalent hands-on experienceFull-stack range. Expert Python plus one of TypeScript, Java, or C# - comfortable across backend, data pipelines, integrations, and a lightweight React surface when a workflow needs oneCloud fluency in Azure or AWS: containers, serverless, managed data stores, and identity and networking inside customer-governed environments (e.g., infrastructure as code with Terraform, Bicep, or CloudFormation), with strong API, distributed-systems, CI/CD, and observability fundamentalsAgentic systems in production. You've built and run single- and multi-agent systems - tool schemas, memory, human-in-the-loop checkpoints - on a modern orchestration framework (e.g., LangGraph, LangChain, Bedrock AgentCore, etc), MCP making practical cost and latency trade-offs (e.g., model selection, caching)Evaluation as engineering. You build golden datasets and eval harnesses from real customer data, and you measure groundedness, task success, and retrieval quality using standard tooling (e.g., RAGAS, LangSmith, Langfuse)Retrieval and data readiness. You've built retrieval systems end-to-end (chunking, embeddings, hybrid search, reranking) over a vector store and can assess and fix messy enterprise data - lineage, permissions, entity resolution, schema mappingKnowledge architecture for AI. You model domain semantics into AI-ready context - ontologies, semantic layers, knowledge graphs - and ground agents in it via GraphRAG or equivalent, using at least one graph technology (e.g., Azure Cosmos DB Gremlin, Amazon Neptune, Neo4j, or equivalent) and its query language (e.g. Gremlin, Cypher, SPARQL)Ownership under ambiguity. You take greenfield customer problems from framing to production and measured outcomes and can explain a non-deterministic system with equal credibility to an engineer and an executiveWork arrangementYou'll work with one customer at a time on focused engagements, for specified duration, on-site at customer locations that may be outside your home country, with travel of up to 75%, subject to any immigration or other requirements (averages likely to be lower across the whole year). You remain an Autodesk employee throughout, and the intention is that Autodesk's policies, benefits, and time off apply on every assignment. Some customers may have their own onboarding, safety, security or other requirements, which will be discussed with you at the timePreferred QualificationsDomain fluency in AECO, with the ability to ramp quickly on design-to-construction-to-operations processesAutodesk platform fluency: Autodesk Platform Services (APS), AEC Data Model API, Autodesk Construction Cloud and Common Data Environment (CDE) concepts, Forma, Fusion, Flow, and Autodesk data modelsEnterprise systems of record integration: ERP, PDM, PLM, BIM, project controls, cost, scheduling, and asset management, plus enterprise data platforms (data lakes, warehouses, Lakehouse, analytics platforms)Constrained enterprise deployment: customer VPCs, private, on-prem, or air-gapped environments, data residency and sovereignty requirements, SSO/SCIM, and formal security review processesAI-native builder practice: routine use of AI coding agents, harnesses, and automation to multiply individual and team throughput, including spec-driven development: writing clear specs and executable acceptance criteria for coding agents, keeping changes small and reviewable . click apply for full job details