Machine Learning Engineering Manager

In this role you lead a ML engineering team to build and operate production-grade AI systems that power real-time inference, batch data products, and agentic solutions. You steer technical direction, collaborate with data, product, and engineering partners, and ensure outcomes translate into business impact. You’ll drive delivery at pace, raise engineering standards, and shape the AI platform’s growth in a fast-moving, purpose-driven environment.

• Lead and grow a team of ML engineers to deliver robust, scalable production systems including real-time APIs, batch products, and LLM-based solutions
• Collaborate with data scientists to move prototypes to production while maintaining quality and performance
• Drive planning, estimation, and sprint delivery to meet deadlines and standards
• Ensure systems shipped deliver measurable outcomes with mechanisms for continual improvement
• Develop team through feedback, mentorship, and career conversations
• Assist in hiring to form a diverse, technically rigorous and commercially aware team
• Contribute to standards and practices across the Data & AI Solutions chapter
• Set engineering quality standards for reproducibility and production-readiness across the team
• Own production health: monitoring, degradation plans, and observability from the start
• Participate in design reviews, architectural decisions, and hands-on sessions when input matters
• Navigate a fast-moving tooling landscape (classical ML, LLMs, agentic AI) and advocate responsible, auditable AI
• Encourage use of AI-assisted development tools to boost pace and quality
• Collaborate with data, product, commercial and engineering leads to translate strategy into well-scoped work
• Represent the team to senior stakeholders and communicate capability and delivery effectiveness
• Shape platform direction by feeding back applied ML requirements

• Experience leading ML/AI engineering teams and ML platforms
• Track record deploying and managing ML/AI systems in production at scale (real-time inference, batch data products, LLM-based solutions)
• Strong Python and ML/AI engineering fundamentals
• Understanding of infrastructure-as-code and CI/CD for ML (Terraform, GitHub Actions, ArgoCD)
• Clear communication able to make technical work accessible to commercial/product audiences
• Experience in agile delivery with accountability for outcomes
• A degree in a quantitative discipline, or equivalent production ML/AI experience

• Clear communication
• Collaborative and cross-functional mindset
• Accountability and delivery focus
• Python
• ML/AI engineering fundamentals
• Infrastructure-as-code (Terraform)