• Design, build, and deploy clinical AI agents that reason over patient context, invoke tools, and generate care recommendations. • Own reliability, observability, and cost efficiency of LLM-powered workflows at scale. • Build and optimize RAG pipelines over clinical knowledge bases, treatment protocols, and real-time patient data. • Develop evaluation frameworks: offline benchmarks, safety tests, regression suites, and LLM-as-judge pipelines wired into CI/CD. • Design multi-step agent orchestration: planning, memory, tool use, error recovery, and human-in-the-loop escalation paths. • Collaborate with clinical, product, and engineering teams to translate patient care needs into AI system design. • Stay at the forefront of AI and engineering best practices, continuously pushing the team to raise the bar on quality, performance, and architecture.

• Design and deliver full stack AI-powered applications from frontend to backend with a focus on scalability and reliability • Build and integrate LLM-powered features using AWS Bedrock, LangChain, and LangGraph • Develop and maintain RESTful and event-driven APIs that power AI workflows • Deploy and manage services on Kubernetes in collaboration with the platform team • Contribute to shared libraries, reusable components, and internal AI enablement tools • Support and mentor AI Developers (SD II) through code reviews and technical guidance • Participate in architecture discussions and help shape engineering standards within the team

• Lead discovery sessions to understand customer systems, workflows, pain points, and objectives. Translate findings into actionable insights. • Develop and maintain a strategic technical roadmap for each account, ensuring alignment between customer goals and Autodesk capabilities. • Design and define tailored technical solutions that demonstrate ROI and align with customer business initiatives. • Coordinate and lead technical specialists to deliver demos, benchmarks, evaluations, and ROI modelling. • Establish credibility through consistent delivery, accurate information, and solution-oriented engagement. • Cultivate long-term technical partnerships with customers to support ongoing success and innovation. • Partner with Account Executives (AEs) to identify cross-sell and upsell opportunities and co-develop customer success plans. • Lead and mentor resolution of complex technical issues escalated by Customer Success.

• You will design, build, and deploy experiments aimed at improving user adoption and engagement. • Formulate hypotheses and execute A/B tests at a high velocity. • Build minimum viable features to validate ideas quickly. • Work seamlessly with Product Managers, Designers, Data Analysts, and Marketing. • Analyze experiment results and user data to uncover hidden insights. • Make smart trade-offs between engineering perfection and speed to market. • Use modern AI tools to accelerate your workflow.