Ofertas › Capital One UK
Capital One UK · London, United Kingdom · £62,000 - 102,000 per year
Salary: £62,000 - 102,000 per year Requirements: We need deep expertise in Python and ML engineering. We need deep expertise in ML/AI systems design, MLOps, and cloud-native architectures. We need a strong track record of leading ML/AI technical initiatives across multiple teams. We need experience with cloud platforms such as AWS, Azure, and GCP. We need experience with ML frameworks such as PyTorch, TensorFlow, and scikit-learn. We need experience with Gen AI and agentic frameworks such as LangGraph, LangChain, VectorDBs, and RAG. We need understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality management. We need experience designing and scaling low-latency, customer-facing ML/AI architectures. We need proven experience setting multi-team ML/AI technical vision and strategy. We need strong technical leadership and influence without authority. We need experience driving ML engineering standards and best practices across organisations. We need a deep understanding of the full ML/AI development lifecycle, including model serving, data pipelines, and Gen AI systems. We need experience leveraging enterprise platforms to deliver business use cases at scale. We need experience steering Communities of Practice or technical forums. We need strong business acumen and the ability to translate ML/AI concepts for different audiences. Responsibilities: We own and drive the ML/AI technical strategy for UK use cases across multiple teams. We lead and coordinate ML engineering efforts, ensuring alignment with business objectives, enterprise platform capabilities, and technology strategy. We provide technical consultancy to teams delivering AI use cases, guiding architectural decisions and solution design. We define and evangelise best practices and reusable approaches that improve delivery of AI use cases across the business. We drive MLOps standards and practices, including CI/CD for models, automated testing, monitoring, and deployment pipelines. We collaborate with enterprise platform and data science teams, contributing to platform capabilities and partnering on use case delivery. We build and maintain strong relationships with senior leadership, product owners, data science teams, and enterprise platform partners. We represent our company in external ML/AI technical forums and industry discussions. We develop strategies to proactively manage technical debt across ML/AI systems. We mentor and develop engineers, f