Agentic AI Business Applications Developer

WHAT YOU DO AT AMD CHANGES EVERYTHING At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you'll discover the real differentiator is our culture. We push the limits of innovation to solve the world's most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career. THE ROLE We have an exciting new opportunity available due to growth for an Agentic AI Business Applications Developer to architect how our team connects to data, exposes capabilities, and delivers high-quality AI outcomes. This is a systems thinking and judgment role — each project is evaluated independently, the right data access strategy is determined from first principles, and the capability layer is built to make AI tools genuinely useful to the people who rely on them. Our team is a highly technical integrated business function, not a software engineering team, and the core of this role is implementing and maintaining AI capability interfaces — MCP-based servers, callable skills, and API endpoints — that connect AI agents to enterprise business systems with the security, governance, and access controls those environments require. AI output quality is owned end to end: testing, evaluating, monitoring, and continuously improving what AI delivers to users. This role owns both strategy and execution — determining the right approach for each project, designing the capability layer that defines what AI can do for our team, and directly shaping the quality of the intelligence our colleagues rely on every day. This role can be remote based within the United States. THE PERSON You are a systems thinker, seeing flows, access patterns, and failure modes before writing code. You are known for your business process awareness, designing for context and meaning, not just technical correctness; as well as being known for your sound judgment on data access strategy, able to assess a project, weigh the tradeoffs, and make a defensible architectural call. Your security-first mindset is the basis for your success in creating governed, auditable, permission-respecting access as a baseline for any AI capability, development and output evaluation. This is a part of your genuine orientation toward output quality, and knowing that what the AI says to the user matters as much as whether the system ran. You have the ability and a good comfort level for operating in ambiguity as the field continues to evolve rapidly. Key Responsibilities Capability Design & AI Interface Architecture • Design the interface layer connecting AI tools and agents to business systems, selecting the right access pattern for each project • Make clear, reasoned decisions about live retrieval vs. caching vs. staging vs. structured storage — and own those decisions • Implement and maintain MCP-based capability interfaces and callable skills with explicit inputs, outputs, and scopes • Design clean, typed request/response contracts optimized for how AI agents reason and respond Security, Governance & Access Control • Design governed access to business systems using RBAC, SSO, and appropriate permission frameworks • Ensure every capability interface respects the security model of the underlying system — authentication, authorization, audit logging, and rate limiting are requirements, not afterthoughts • Evaluate data sensitivity and access requirements before connecting any new system to the AI capability layer Enterprise Systems & Data Integration • Connect AI capabilities to enterprise business platforms — including analytics tools, collaboration systems, graph APIs, relational databases, and other organizational data sources • Write Python and SQL to query, transform, and shape data from enterprise sources for AI consumption • Understand the business processes behind the data — design for business context, not just schema • Define and consume REST and GraphQL APIs; write OpenAPI specs and JSON Schema definitions AI Output Quality & Evaluation • Design and run evaluation frameworks measuring accuracy, relevance, groundedness, and hallucination rates • Diagnose root causes when AI tools produce poor or inaccurate responses, tracing failures to interface design, retrieval strategy, or data quality • Establish quality standards across every capability the team builds and continuously improve based on observed outcomes Documentation, Patterns & Enablement • Build production-quality documentation for every capability, endpoint, and integration • Develop reusable patterns and frameworks that make building new capabilities