AI Developer – Rack Systems Engineering at Advanced Micro Devices, Inc

Markham, Ontario, Canada -

Full Time

Start Date

Immediate

Expiry Date

05 Jul, 26

Salary

0.0

Posted On

06 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, LLM, AI Development, Workflow Automation, Rack-scale Hardware, Data Processing, Orchestration, GitHub, VS Code, Cursor, System Design, Firmware Engineering, Hardware Development, Technical Leadership, Debugging, Validation

Industry

Semiconductor Manufacturing

Description

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 The AI Developer – Rack Systems Engineering drives AI-driven work automation and intelligent tooling directly into AMD’s rack-scale hardware workflows. This senior technical role designs and implements Python-based and LLM-powered agents that automate and streamline how rack systems are planned, configured, validated, debugged, reported, and managed across the organization. You will work at the intersection of AI development and rack-level hardware execution, turning complex, cross-discipline engineering activities into reliable automated workflows that reduce manual effort, shorten cycle time, improve process consistency, and increase predictability for leadership and partner teams. As an SMTS, you are expected to solve complex, non-recurring problems, lead significant changes in existing processes, operate with minimal supervision, and mentor other developers and engineers. Key Responsibilities AI Development for Rack-Scale Hardware Workflows Design and build Python-based AI agents, automations, and workflow pipelines that support rack-level configuration, readiness tracking, debug workflows, execution reporting, and day-to-day work automation for rack systems. Develop LLM-driven tools and multi-agent workflows that transform existing rack-systems processes, checklists, spreadsheets, and tribal knowledge into reliable, reusable, and increasingly automated AI flows aligned with DPEG’s AI enablement roadmap. Implement robust data processing, orchestration, and integration logic that connects AI agents to design, planning, validation, and reporting sources used by rack-systems teams. Identify repetitive, manual, and time-consuming engineering tasks and convert them into scalable AI-assisted or fully automated workflows that improve execution speed and consistency. Cross‑Functional Collaboration Collaborate closely with the following groups to ensure AI workflows reflect real rack‑scale engineering needs and constraints: Firmware Engineering – Ingest and structure firmware inputs, status, and configuration requirements into AI‑driven flows supporting rack integration and debug. Product Ops – Embed AI agents into product and rack‑systems process development, capacity views, and execution dashboards to reduce manual reporting and improve decision speed. System Design – Align AI logic with system‑level architectures, design constraints, and configuration rules at the rack level. Quality Engineering – Use AI agents to surface risks, coverage gaps, and recurring issue patterns from quality and defect data. PC Board Design – Support board‑level inputs to rack configurations (BOMs, options, constraints) via AI‑assisted extraction and transformation. Hardware Development – Ensure AI workflows accurately represent hardware states, dependencies, and readiness as racks move through development milestones. Failure Engineering – Apply AI‑driven triage, pattern detection, and summarization across failure analysis artifacts, logs, and lessons‑learned to feed back into rack‑systems processes. Systems Architecture – Capture architectural rules, design intents, and trade‑offs into AI logic used to guide rack‑level decisions. Testing and Validation – Automate test‑result aggregation, coverage summaries, and risk views using AI agents integrated into validation workflows and dashboards. Enterprise‑Ready AI Implementation Use modern AI-assisted developer tools such as GitHub, VS Code, Cursor, Claude-based tools, and OpenAI-style code agents to rapidly prototype, automate, and harden rack-focused AI workflows. Implement AI solutions within AMD’s enterprise AI environment, adhering to internal security, governance, and deployment patterns. Design workflows that can scale across teams while respecting data boundaries, confidentiality, and applicable AI security standards. Build automations that are maintainable, auditable, and suitable for repeated use in production engineering environments rather than one-off prototypes. Technical Leadership Own end-to-end AI solution definition for selected rack-systems workflows: problem framing, automation opportunity identification, architecture, implementation, deployment, and handoff to ongoing owners. Drive significant improvements in how rack-systems processes are executed, reducing manual steps and accelerating time-to-insight for leadership. Advise and guide peers and cross-team representatives in AI development techniques, workflow automation approaches, AI-tool usage, and best practices for integrating AI into hardware-centric workflows. Required Experience & Skills Strong, proven Python development capability applied to automation, data processing, or system integration. Hands‑on experience building and deploying LLM‑driven agents, workflows, or tools that deliver measurable impact (e.g., time savings, quality improvement, reduced manual effort). Proficiency with GitHub and modern development workflows (branching, reviews, CI). Familiarity with AI‑assist coding environments such as Cursor, Claude‑based code tools, or similar agentic coding platforms. Experience with HW development process and workflow. Experience with board design desirable, experience with system design a big plus. Proven track record operating at the following level: Solving complex, non‑recurring technical problems Making technical decisions with 6–12‑month impact Working with minimal supervision Mentoring less‑experienced engineers and influencing multi‑team technical direction ACADEMIC QUALIFICATIONS: Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, Electrical Engineering, Systems Engineering, or related field. Advanced degree is a plus. #LI-KW1 Benefits offered are described: AMD benefits at a glance. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

Responsibilities

The AI Developer will design and implement Python-based and LLM-powered agents to automate rack-scale hardware workflows, including configuration, validation, and reporting. They will collaborate across engineering teams to integrate these AI solutions into existing hardware development processes to improve efficiency and consistency.