Job Requisition ID #25WD94058 Presentation du poste Si vous aimez développer des systèmes concrets utilisés par de vrais clients — et que les modèles de langage à grande échelle (LLM), le RAG, le MCP et les architectures agentiques vous passionnent véritablement — ce poste est fait pour vous. Position Overview If you love building real systems that real customers use—and you get genuinely excited about LLMs, RAG, MCP, and agentic architectures—this role is for you. The Applied AI team in Autodesk's Data and Process Management (DPM) organization ships Cloud-Native AI agents that make our Product Data Management (PDM) and Product Lifecycle Management (PLM) workflows smarter and easier. This is a hands‑on architecture role where you'll design core platform patterns and also write code that lands in production. Responsibilities Architect and build scalable, secure cloud‑native services and Agentic AI workflows that run in production Own the GenAI/ML architecture for production agentic systems: tool‑use, orchestration, state/memory, routing, and multi‑step workflows Define model strategy across prompting, retrieval (RAG), and fine‑tuning—making tradeoffs using measurable quality, latency, safety, and cost Standardize tool/context integrations across internal systems using MCP‑based patterns (or equivalent approaches), enabling teams to ship faster on a shared foundation Establish evaluation + observability standards (regression tests, monitoring, feedback loops) Set technical direction via reference architectures, best practices, and hands‑on technical guidance across teams Partner closely with Product, Security/Privacy, and Engineering leaders to deliver high‑impact features Minimum Qualifications Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or equivalent practical experience 8+ years building cloud‑native software in production (distributed systems, APIs, data‑intensive services, reliability and operations) 4+ years delivering AI/ML‑powered system in production Traditional ML cloud applications (training pipelines, deployment, monitoring, iteration) LLM‑based systems (RAG, MCP, Agent workflows, fine‑tuned models) Experience with MCP or similar standardized patterns for connecting models to tools and context Experience with deploying and maintaining AI Applications in production reliably, monitoring performance, and improving over time Proficiency in Python/TypeScript/Java with strong engineering fundamentals (testing, code quality, performance, security) Strong communication skills: you can explain tradeoffs clearly and influence decisions without relying on authority Preferred Qualifications Deep experience designing AI evaluation pipelines and production release strategies for AI Applications Experience with AWS/Azure/GCP and modern platform practices (containers, Kubernetes, CI/CD, observability) Experience in PLM/PDM, manufacturing, CAD, or enterprise workflow software Open‑source contributions, publications, or talks related to distributed systems, ML or GenAI systems #J-18808-Ljbffr