Machine Learning Engineer

Overview

In this role you bring production-ready ML to real-world financial services challenges, shaping scalable ML software and scalable architectures. You’ll collaborate with cross-functional teams to ensure feasible, timely delivery and set standards for deployment at scale. You’ll translate complex ML concepts for stakeholders and act as a technical advisor to clients and partners. This is an opportunity to drive responsible AI in regulated industries and elevate client outcomes.

Pay / Benefits

• Unlimited Annual Leave Policy
• Private healthcare and dental
• Enhanced parental leave
• Family-Friendly Flexibility & Flexible working
• Sanctus Coaching
• Hybrid Working (2 days in London office)

Responsibilities

• Build and deploy production-grade ML software, tools, and infrastructure
• Create reusable, scalable solutions to accelerate ML delivery
• Collaborate with engineers, data scientists, and commercial leads to address client challenges
• Lead technical scoping and architectural decisions for project feasibility and impact
• Define and implement Faculty’s standards for deploying ML at scale
• Serve as a technical advisor to customers and partners, translating ML concepts for stakeholders

Key requirements

• Experience operationalising models with Scikit-learn, TensorFlow, or PyTorch
• Strong Python programming and software engineering practices
• Hands-on cloud platform experience (AWS, Azure, GCP) including architecture and security
• Experience with Docker and Kubernetes for scalable applications
• Solid understanding of core ML concepts (probability, statistics, learning techniques)
• Excellent communication to guide technical teams and advise non-technical stakeholders
• Thrives in fast-paced environments with autonomous scope ownership

• Excellent communication
• Self-driven and autonomous
• Collaborative and cross-functional mindset
• Machine learning lifecycle expertise
• Production-grade ML deployment
• Cloud architecture and security

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