Senior AI/ML Platform Engineer

Role Summary

Senior AI/ML Platform Engineer to help realize a vision of transformative AI and machine learning across GSK's data ecosystem. The role sits within the AI/ML Platform Engineering team, which is building a first-in-class platform of tools and services covering MLOps/DevOps across Cloud and High-Performance Computing. The goal is to decrease development time and raise the quality bar on engineering across AI/ML teams and products. The team emphasizes ownership, accountability, continuous development, and collaboration, with career development supported from day one.

Responsibilities

• Serve as a key engineer for the AIML platform and contribute technical expertise to teams in closely aligned technical areas such as GenAI Platform, DevOps, Compute and Cloud.
• Lead design of major software components of the AIML Platform and contribute to development of production code in Python and participate in both design reviews and PR reviews.
• Accountable for key component(s) of AIML Platform with particular focus on usability, reproducibility and performance at scale.
• Integrate with DataOps, HPC and Data Engineering products for best performance and ease of use in ML training at scale.
• Participate in or lead project teams and contribute technical expertise to teams in closely aligned technical areas.
• Able to design innovative strategies and ways of working to create a better environment for the end users.
• Champion best practices in ways of working and engineering discipline, and proactively contribute to improvements within your engineering area.

Qualifications

• Required:
  • Bachelor’s, Master’s or PhD degree in Computer Science, Software Engineering, or related discipline.
  • 6+ years of experience in industry experience in software engineering with a Bachelor’s.
  • 4+ years of experience in industry experience in software engineering with a Master’s.
  • 2+ years of experience in industry and/or academic experience in software engineering with a PhD.
  • 2+ years of experience in AIML engineering, including large-scale model training and production deployment.
  • Experience with delivering projects primarily using Python.
• Preferred:
  • Deep knowledge and use of Python programming language including toolchains for documentation, testing, and operations / observability
  • Deep expertise in modern software development tools / ways of working (e.g. git/GitHub, DevOps tools, metrics / monitoring, …)
  • Deep cloud expertise (e.g., AWS, Google Cloud, Azure), including infrastructure-as-code tools (Terraform, Ansible, Packer, …) and scalable cloud compute technologies, such as Google Batch and Vertex AI
  • Deep hands-on experience with ML frameworks such as PyTorch or TensorFlow as well as external libraries such as Huggingface and/or Deepspeed
  • Hands-on experience with frameworks for building agentic AI systems, such as LangGraph, LangChain
  • Experience with ML application performance tuning and optimization, both for ML training and inference/deployment, including large scale multi-GPU, and/or multi-TPU multi-node distributed training for large models such as LLMs
  • Experience with CI/CD implementations using git and a common CI/CD stack (e.g., Azure DevOps, CloudBuild, Jenkins, CircleCI, GitLab)
  • Experience in ML workflow orchestration and pipelines with tools such as Vertex Pipelines, MLFlow, etc.
  • Experience with MLOps tools and model deployments (including LLMs) such as Kubeflow, Vertex AI Predictions, vLLM, Ollama
  • Deep expertise with Docker, Kubernetes, and the larger CNCF ecosystem including experience with application deployment tools such as Helm
  • Experience with High-Performance Computing (HPC) at both software stack and hardware level and understanding performance within HPC systems
  • Deep familiarity with tools, techniques, optimizations in AIML and AIML Platform/MLOps space, including engagement with the open-source community
  • Demonstrated excellence with agile software development environments using tools like Jira and Confluence

Education

• Bachelor’s, Master’s or PhD degree in Computer Science, Software Engineering, or related discipline