Staff Software Engineer - Machine Learning

Capital One Capital One · Banking · London, United Kingdom

Staff Software Engineer - Machine Learning role at Capital One in London. This role focuses on owning and driving the ML/AI technical strategy for UK use cases, leading ML engineering efforts across multiple teams, and providing technical consultancy. It involves defining best practices, driving MLOps standards, and collaborating with data science and enterprise platform teams. The role requires deep expertise in Python, ML engineering, MLOps, cloud-native architectures, ML frameworks, and Gen AI/Agentic frameworks like LangGraph, LangChain, VectorDBs, and RAG. Experience in designing and scaling low-latency, customer-facing ML/AI architectures and understanding responsible AI practices are also key.

What you'd actually do

  1. Own and drive the ML/AI technical strategy for UK use cases, spanning multiple teams and influencing the overall technical direction for AI adoption
  2. Lead and coordinate ML engineering efforts across multiple teams, ensuring alignment with broader business objectives, enterprise platform capabilities, and technology strategy
  3. Provide technical consultancy to teams delivering AI use cases, guiding architectural decisions, solution design, and effective use of enterprise ML/AI platforms and capabilities
  4. Proactively identify emerging ML/AI patterns, define and evangelise best practices, and establish reusable approaches that enhance delivery of AI use cases across the business
  5. Drive MLOps standards and practices across teams, including CI/CD for models, automated testing, monitoring, and deployment pipelines

Skills

Required

  • Deep expertise in Python and ML engineering
  • Deep expertise in ML/AI systems design, MLOps, and cloud-native architectures
  • Track record of leading ML/AI technical initiatives across multiple teams
  • Strong experience with cloud platforms (AWS, Azure, GCP)
  • Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn)
  • Experience with Gen AI/Agentic frameworks (LangGraph, LangChain, VectorDBs, RAG)
  • Understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality management for AI systems
  • Experience designing and scaling low-latency, customer-facing ML/AI architectures
  • Proven experience setting a multi-team ML/AI technical vision and strategy
  • Strong track record of technical leadership and influence without authority
  • Experience driving ML engineering standards and best practices across organisations
  • Deep understanding of the full ML/AI development lifecycle, including model serving, data pipelines, and Gen AI systems
  • Experience leveraging enterprise platforms to deliver business use cases at scale

Nice to have

  • Experience of steering Communities of Practice or technical forums
  • Strong business acumen and ability to translate ML/AI concepts for various audiences

What the JD emphasized

  • ML/AI technical strategy
  • ML engineering efforts across multiple teams
  • technical consultancy to teams delivering AI use cases
  • MLOps standards and practices
  • Gen AI/Agentic frameworks
  • low-latency, customer-facing ML/AI architectures
  • technical leadership and influence without authority
  • full ML/AI development lifecycle

Other signals

  • ML/AI technical strategy
  • ML engineering efforts across multiple teams
  • technical consultancy to teams delivering AI use cases
  • MLOps standards and practices
  • Gen AI/Agentic frameworks