Senior Machine Learning Engineer

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October 30

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Williams Lea

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Enterprise • B2B • SaaS

Williams Lea is a global provider of business and administrative support services, specializing in presentations, creative services, secretarial support, document and word processing, billing, collections, and more. They offer tailored services to industries such as legal, financial, and professional services firms, aimed at enhancing operational efficiency and cost reduction. Their ENGAGE technology enables companies to leverage AI for improved service quality and performance in a virtual and digital environment. Operating worldwide, Williams Lea supports businesses with digitized, high-touch, and connected services designed for hybrid workforces in regulated environments.

5001 - 10000 employees

🏢 Enterprise

🤝 B2B

☁️ SaaS

📋 Description

• 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, NumPy) and frameworks like TensorFlow or PyTorch • Develop and deploy ML solutions on AWS, particularly using Amazon SageMaker • Leverage AWS services (Lambda, S3, Redshift, CloudWatch) to build end-to-end solutions • Own and improve CI/CD pipelines using Infrastructure as Code (Terraform, CloudFormation) • Work closely with product teams, DevOps, 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 • 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

🎯 Requirements

• 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, NumPy, and at least one deep learning framework (e.g. TensorFlow, PyTorch) • Deep understanding of ML algorithms, modelling techniques, and performance evaluation methods • Hands-on experience with AWS cloud services, including SageMaker • Experience with CI/CD practices, Docker, and Infrastructure-as-Code tools like Terraform or CloudFormation • Solid understanding of MLOps principles and how to productionize ML systems in a scalable, maintainable way • Experience leading small teams or mentoring engineers in a collaborative, agile environment

🏖️ Benefits

• 25 days holiday, plus bank holidays • Salary sacrifice schemes, retail vouchers • Life Assurance • Private Medical Insurance • Dental Insurance • Health Assessments • Cycle-to-work scheme • Discounted gym memberships • Referral Scheme