Senior Machine Learning Engineer

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Senior Machine Learning Engineer

Up to £80,000 per annum depending on experience, plus company benefits

Full time, permanent

37.5 hours per week Mon‑Fri, 8:30am‑5pm with a 1‑hour unpaid break

Fully remote

Williams Lea seeks a Senior Machine Learning Engineer to join our team! Williams Lea is the leading global provider of skilled, technology‑enabled, business‑critical support services, with long‑term trusted relationships with blue‑chip clients across investment banks, law firms and professional services firms. Williams Lea employees, nearly 7,000 people worldwide, provide efficient business services at client sites in often complex and highly regulated environments, from centralised Williams Lea onshore facilities, and through best cost company offshore locations.

As a Senior Machine Learning Engineer, you will play a central role in designing, developing, and scaling AI‑powered solutions that address complex challenges in highly regulated industries such as legal and investment banking.

Working as part of a global engineering organisation — and reporting to the Lead ML Engineer — you will combine technical excellence, hands‑on development, and team leadership. You’ll help shape the Machine Learning Centre of Excellence, contributing to the direction of our engineering practice while mentoring junior engineers and collaborating across teams to deliver impactful solutions.

This role requires someone with real‑world experience bringing ML/AI services to market at scale, strong communication skills, and the ability to collaborate with internal stakeholders, client teams, and partners — including AWS specialists.

If you're a curious, driven engineer with a passion for building smart, scalable AI solutions — and mentoring others while you do it — this is the role for you.

• Lead the design and implementation of scalable ML models and data pipelines to support AI‑powered products in regulated domains
• Translate business challenges into technical ML solutions using the most appropriate algorithms, models, and tools
• Build, train, and evaluate models using Python (e.g. scikit‑learn, pandas, Num Py) and frameworks like Tensor Flow or Py Torch
• Develop and deploy ML solutions on AWS, particularly using Amazon Sage Maker
• Leverage AWS services (Lambda, S3, Redshift, Cloud Watch) to build end‑to‑end solutions
• Own and improve CI/CD pipelines using Infrastructure as Code (Terraform, Cloud Formation)

• Work closely with product teams, Dev Ops, data scientists, and external AWS partners to deliver reliable ML services
• Contribute to team‑wide decision‑making on architecture, toolsets, and process improvements
• Communicate ML concepts and solution rationale clearly to non‑technical stakeholders and clients

• Provide technical leadership to mid‑level and junior ML engineers, including reviewing code, guiding experiments, and setting best practices
• Foster a culture of collaboration, curiosity, and continuous improvement
• Contribute to the growth of our global ML engineering team, including upskilling colleagues in India

• Ensure models and ML pipelines meet performance, accuracy, and compliance standards
• Maintain documentation for all stages of the ML lifecycle — from data pre‑processing to deployment workflows
• Follow data security protocols and best practices in regulated environments

• 4–6 years of hands‑on experience in machine learning engineering or data science roles
• Proven success in building and deploying AI/ML services at scale, ideally in regulated sectors (e.g. finance, legal, healthcare)
• Strong programming skills in Python and proficiency with libraries such as scikit‑learn, pandas, Num Py, and at least one deep learning framework (e.g. Tensor Flow, PyTorch)
• Deep…