• Own AI-powered features end-to-end, from scoping, design and requirements engineering to feature evaluation, deployment and operation. • Design, build and maintain cloud-native, event-driven AI applications across the full stack. • Evaluate, integrate, and optimize AI models for production use, balancing quality, latency, reliability and cost. • Collaborate effectively with cross-functional team members, including non-technical stakeholders, to refine requirements and align technical solutions with business objectives. • Build and maintain robust AI inference, evaluation, and monitoring pipelines. • Develop and implement robust, automated AI application evaluation frameworks. • Analyze AI application performance data and experiments, using insights for data-driven improvements. • Operate and improve production systems, taking ownership of reliability, quality, and technical debt. • Stay current with AI advancements and share knowledge to upskill the team. • Plan, track, and break down work effectively in an agile team. • Provide technical leadership, drive design reviews, write ADRs and contribute to hiring and onboarding. • Participate in on-call rotations, incident response and post-mortems for AI-powered systems and help define SLOs and error budgets.

• Serve as the engineering lead for Agentic AI delivery across Supply Planning and Manufacturing — owning the design, development, and deployment of production-grade AI agent solutions. • Architect and build multi-agent AI systems using Azure AI Agent Service, AutoGen, Semantic Kernel, and/or LangChain/LangGraph — including orchestrator-executor patterns, tool calling, memory management, and agent coordination. • Implement the MCP to surface enterprise data as structured context for AI agents operating in supply chain and manufacturing workflows. • Build and deploy generative AI solutions on Azure AI Foundry — RAG-based knowledge agents, decision support for forecasting and capacity planning, and document intelligence for maintenance work orders and recipes. • Design and deliver AI copilots and topic-based agents using Microsoft Copilot Studio — enabling Supply Planning and Manufacturing teams to access insights and take action directly from Teams and Outlook. • Act as the AI delivery owner for agentic use cases — scoping business problems with stakeholders, defining agent capabilities and tool surfaces, prioritizing the roadmap, and driving adoption. • Apply emerging agentic AI patterns — including ReAct, Plan-and-Execute, reflection, and human-in-the-loop — for supply chain and operational use cases. • Partner with Supply Chain leadership, Demand Planning, Process Engineering, Maintenance Ops, and Plant teams to identify, scope, and deliver AI use cases that influence operational decisions. • Define and maintain AI agent governance — prompt versioning, tool auditing, evaluation frameworks, observability, and safety guardrails for production deployments. • Develop on Azure Databricks — PySpark and SQL against gold/platinum Delta tables, notebooks for transformation and feature work, and orchestration via Workflows. • Build and maintain Power BI reports and semantic models that serve as grounding data for AI agents and executive dashboards across Supply Planning and Manufacturing. • Own Supply Chain AI metrics alignment cadence — keeping priorities, status, and roadblocks visible to Supply Chain and Manufacturing leadership. • Mentor analysts and engineers on agentic AI design patterns, MCP, and AI delivery best practices.

• Drive the development of AI-powered solutions • Collaborate with product and engineering teams • Lead a team of AI engineers and data scientists • Establish best practices for AI and GenAI deployment • Stay informed about advancements in AI

• Design, develop, and deploy advanced AI solutions • Lead a cross functional team to drive AI solution delivery • Develop, test, and deploy AI solutions in cloud and on-premises • Work with large healthcare datasets, performing data preprocessing, feature engineering • Implement and optimize AI models within Gainwell’s existing technology stack • Continuously monitor, refine, and optimize AI models for accuracy and efficiency • Ensure AI implementations adhere to healthcare industry regulations