Agentic AI Software Applications Developer

Advanced Micro Devices, Inc•Austin, TX

9h•Remote

About The Position

We have an exciting new opportunity available due to growth for an Agentic AI Software Application 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.

Requirements

Systems thinker, seeing flows, access patterns, and failure modes before writing code.
Business process awareness, designing for context and meaning, not just technical correctness.
Sound judgment on data access strategy, able to assess a project, weigh the tradeoffs, and make a defensible architectural call.
Security-first mindset, creating governed, auditable, permission-respecting access as a baseline for any AI capability, development and output evaluation.
Genuine orientation toward output quality, knowing that what the AI says to the user matters as much as whether the system ran.
Ability and good comfort level for operating in ambiguity as the field continues to evolve rapidly.
Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field.

Nice To Haves

Understanding and experience in systems design, API development, platform engineering, or solutions architecture
Experience integrating with enterprise business platforms such as analytics tools, collaboration systems, and relational databases
Working knowledge of RBAC, SSO, and governed access control in enterprise environments
Python and SQL proficiency — comfortable querying, transforming, and integrating data from enterprise systems
Hands-on experience designing and documenting APIs (OpenAPI, JSON Schema, REST, GraphQL) in production
Active daily use of modern AI developer tools as a builder — using these tools to build things, not just ask questions
Demonstrated ability to decompose business processes into discrete, callable capabilities with clear contracts
Track record of shipping production-grade systems, not just proofs of concept
Experience implementing or configuring MCP servers or AI agent tool/skill definitions
Experience designing AI evaluation frameworks (hallucination, relevance, groundedness)
Background in semiconductor, hardware, or high-tech manufacturing — familiarity with engineering data, supply chain, PLM, or EDA systems is a plus
Familiarity with RAG architectures and how retrieval design affects AI output quality
Experience in a business-embedded, non-IT team making independent integration and architecture decisions

Responsibilities

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
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
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
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
Build production-quality documentation for every capability, endpoint, and integration
Develop reusable patterns and frameworks that make building new capabilities faster and more consistent
Train team members on AI-friendly system design, capability decomposition, and security considerations
Stay current with the evolving AI tooling and agent ecosystem and bring relevant advancements back to the team