Machine Learning Engineering & Applied AI ML Lead - Vice President
On-site · London, England, United Kingdom
Job Summary
Lead the machine learning engineering efforts in the Applied AI/ML team for enterprise banking, designing and delivering production ML architectures, collaborating with cloud/SRE, translating research into scalable AI-powered products, mentoring team members, and applying MLOps practices to versioning, observability, and reproducibility. The role emphasizes building enterprise-grade, data-intensive applications, leveraging AWS/Kubernetes, PyTorch/NumPy, distributed systems, and reusable libraries, with optional management responsibilities and a focus on aligning ML work with business objectives.
Required Qualifications
- Experience in machine learning engineering roles
- Degree in a quantitative discipline (Computer Science, Mathematics, Statistics)
- Proven ability to develop and deploy business-critical, data-intensive applications
- Extensive experience with AWS and Kubernetes
- Proficiency with lower-level libraries such as PyTorch and NumPy
- Hands-on experience implementing distributed, multi-threaded, and scalable applications
- Experience with automated building, testing, and deployment pipelines
- Familiarity with higher-level interfaces like Pydantic AI and Langraph
- Strong understanding of computer science fundamentals and development best practices
- Broad knowledge of MLOps tooling for versioning, reproducibility, and observability
- Ability to understand business objectives and align ML problem definitions
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