Senior Machine Learning Engineer
Williams Lea
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Benefits offered by this job
Job summary
Qualifications
- 4–6 years of hands-on experience in machine learning engineering.
- Proven success in building and deploying AI/ML services at scale.
- Deep understanding of ML algorithms and performance evaluation methods.
Responsibilities
- Lead the design and implementation of scalable ML models.
- Translate business challenges into technical ML solutions.
- Develop and deploy ML solutions on AWS using SageMaker.
Skills
Education
Tools
Job description
Join to apply for the Senior Machine Learning Engineer role at Williams Lea
This range is provided by Williams Lea. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Direct message the job poster from Williams Lea
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, 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
- 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, 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 productionise ML systems in a scalable, maintainable way
- Experience leading small teams or mentoring engineers in a collaborative, agile environment
- Exposure to legal tech, contract analytics, or financial modelling using NLP, classification, or predictive models
- Experience working in cross‑functional, geographically distributed teams
- Familiarity with MLOps tools like MLflow, Kubeflow, or Apache Spark
- Relevant certifications (e.g. AWS Certified Machine Learning – Specialty, TensorFlow Developer)
- Strong problem‑solving mindset and ability to break down complex challenges into practical, scalable ML solutions
- A creative engineer with a scientific approach — balancing experimentation with execution
- Naturally curious, self‑motivated, and constantly looking to grow and help others do the same
- Comfortable working both autonomously and collaboratively
- Clear, confident communicator able to work across technical and non‑technical teams
We believe in supporting our employees in both their professional and personal lives. As part of our commitment to your well‑being, we offer a comprehensive benefits package, including but not limited to:
- 25 days holiday, plus bank holidays (pro‑rata for part time roles)
- Salary sacrifice schemes, retail vouchers – including our TechScheme which can be used on a range of gadgets such as Smart TVs, laptops and computers or household appliances.
- Life Assurance
- Private Medical Insurance
- Health Assessments
- Discounted gym memberships
- Referral Scheme
The Company values the differences that a diverse workforce brings to the organisation and will not discriminate because of age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race (which includes colour, nationality and ethnic or national origins), religion or belief, sex or sexual orientation (each of these being a “protected characteristic” in discrimination law). It will not discriminate because of any other irrelevant factor and will build a culture that values openness, fairness and transparency.
If you have a disability and would prefer to apply in a different format or would like to make a reasonable adjustment to enable you to make an interview please contact us at careersatWL@williamslea.com (we do not accept applications to this email address).
View our Privacy Notice https://www.williamslea.com/privacy-statement
** Please note: We can only consider candidates who are currently based in England and have the legal right to work in the UK. **
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LONDON, 2025-11-01
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