Senior AI​/ML Infrastructure Engineer

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.

AMD is looking for a specialized software engineer who is passionate about improving the performance of key applications and benchmarks. You will be a member of a core team of incredibly talented industry specialists and will work with the very latest hardware and software technology.

The ideal candidate should be passionate about software engineering and possess leadership skills to drive sophisticated issues to resolution. Able to communicate effectively and work optimally with different teams across AMD.

• Architect and scale end-to-end infrastructure for training, fine‑tuning, and deploying machine learning and large language models (LLMs) across distributed, hybrid cloud, and on‑prem environments.
• Design and maintain MLOps/LLMOps pipelines, ensuring seamless integration between data ingestion, model training, evaluation, and production deployment.
• Collaborate closely with AI researchers, data scientists, and platform engineers to enable efficient experimentation, versioning, and continuous improvement of models.
• Implement and manage robust CI/CD and CI/CT (Continuous Training) pipelines, automating testing, validation, and deployment workflows for ML/LLM systems.
• Develop tools and frameworks for model serving, latency optimisation, and inference performance tuning, leveraging GPU acceleration and distributed systems best practices.
• Monitor and improve production ML systems—track drift, performance degradation, and operational efficiency; implement alerting and rollback strategies.
• Drive software engineering excellence, emphasising code quality, testability, and scalability for all ML infrastructure components.
• Optimize compute and storage resources across cloud and on‑prem clusters, ensuring cost efficiency and high utilisation.
• Stay current with the latest advancements in ML orchestration, LLM optimisation (quantisation, distillation, caching), and deployment frameworks.
• Document, communicate, and mentor—promote best practices in reproducibility, version control, and model governance.

• MLOps / LLMOps Expertise:
  Proven experience building and maintaining production ML pipelines, model registries, and deployment systems for large‑scale AI workloads.
• Software Development:
  Strong background in systems engineering and backend development (Python preferred), with deep understanding of distributed systems, APIs, and infrastructure reliability.
• Deep Learning Frameworks:
  Proficiency with PyTorch, Tensor Flow, and LLM tool chains (Hugging Face, Deep Speed, vLLM, etc.).
• Infrastructure & Cloud:
  Familiarity with Kubernetes, Docker and experience with on‑prem GPU clusters is a plus.
• CI/CD & Automation:
  Strong understanding of Git Ops, continuous integration/testing/deployment systems (e.g., Jenkins, Git Hub Actions, Dagster, Airflow).
• Optimization & Performance:
  Hands‑on experience with model and system‑level optimisations, including quantisation, batching, caching, and hardware utilisation tuning.
• Problem‑Solving:
  Demonstrated ability to debug complex distributed systems, resolve performance bottlenecks, and drive root‑cause analysis in production ML environments.
• Collaboration:
  
  Excellent communication skills and the ability to work cross‑functionally with research, data, and product teams.
• Continuous Learning:
  Track record of quickly adapting to new ML tools, frameworks, and technologies in a fast‑evolving AI ecosystem.

• Bachelo…