• Build LLM features from ideation to production, including scaling. • Develop benchmarks to evaluate LLM performance and mitigate hallucinations. • Own the full development lifecycle, including infrastructure and front-end work around AI features. • Foster a constructive environment through code reviews, mentoring, and knowledge sharing.

• Build agent capabilities for planning, tool use, memory, and context management, and ship them into production. • Integrate agents with internal and external tools and data sources (retrieval systems, structured datasets, lab/biomed APIs, spreadsheets, search), with robust schemas and safeguards. • Develop quality and evaluation systems, including unit, regression, and scenario/benchmark tests, telemetry, and automated scoring. • Collaborate with scientists to analyze failure modes and improve performance. • Partner with the knowledge and ontology team to ensure outputs are source-traceable and compliant with provenance standards. • Implement safety measures, guardrails, and sandboxed execution for risky operations. • Optimize performance and reliability through profiling, idempotency, retries, rate limiting, and uptime management. • Instrument data pipelines for supervised fine-tuning and reinforcement learning when needed. • Contribute to the agent platform, including services, APIs, orchestration, CI/CD, and observability.

• Own and maintain prompt engineering strategies, including prompt versioning, testing, and optimization • Design AI workflows that combine models, prompts, tools, enterprise data, and business logic • Implement AI orchestration layers to manage multi-step reasoning, decisioning, and actions • Ensure AI systems integrate cleanly into business workflows, APIs, and user interfaces • Apply guardrails to ensure safe, explainable, and compliant AI behavior • Support in building and maintaining production-grade deployment pipelines for AI solutions • Ensure reliability, scalability, cost control, and latency optimization • Implement monitoring and observability for AI systems (usage, performance, drift, failures) • Define and enforce change control, versioning, rollback, and release management processes • Collaborate closely with data scientists, actuaries and other business functions • Validate model behavior, outputs, and assumptions from a production and business-use perspective

• Design, develop, and maintain MCP servers that expose external tools and capabilities to AI assistants. • Implement specific MCP tools (functions) for reading files, managing issues, interacting with databases, or triggering complex workflows. • Define and structure resources, schemas, and prompts accessible to AI agents. • Ensure security and access control for the MCP server, limiting which tools and data agents can execute or access. • Manage API integrations and external data sources used by MCP tools. • Use JSON Schema or similar standards to document and validate tool definitions. • Integrate MCP Server tools with Azure AI Foundry agents, enabling AI agents to perform actions such as data querying, workflow automation, and API interaction. • Collaborate with product managers and front-end teams to integrate AI capabilities into user-facing platforms (e.g., Microsoft Copilot, VS Code extensions, or web portals). • Utilize Azure Functions, Azure Logic Apps, and Azure Cognitive Services to connect MCP-based AI workflows to enterprise systems. • Develop and manage tools that interface with Azure resources (e.g., Azure Storage, Azure SQL, Azure Compute Services) through the MCP Server. • Enable AI agents to securely manage, retrieve, and utilize Azure resources. • Apply best practices in cloud security, authentication, and API permissions management. • Conduct comprehensive testing and debugging of MCP tools and Azure integrations. • Create detailed documentation for MCP tools, including usage examples, API specifications, and integration guides. • Ensure scalability, performance, and reliability of MCP and AI Foundry deployments. • Partner with AI engineers, cloud architects, and data scientists to align tool development with strategic goals. • Present technical solutions and architectural decisions to stakeholders in clear, actionable terms. • Contribute to the AI governance framework, ensuring responsible AI tool design and usage.