Machine Learning Engineer

Machine Learning Engineer | Generative AI | AWS | End-to-End ML Solutions

Location: London-based | Hybrid

Please note: Sponsorship is not offered for this position

We’re working with a leading organisation on a mission to become one of the most insight-driven businesses in its sector, placing machine learning and generative AI at the core of customer experiences, operational optimisation, and strategic decision-making.

This is a fantastic opportunity for a skilled Machine Learning Engineer to build and deploy scalable, production-level ML solutions, working closely with Data Scientists and cross-functional teams to drive measurable business impact.

You’ll play a pivotal role in integrating machine learning models into end-to-end pipelines, supporting strategic initiatives such as customer engagement, automated insights, and decision-support tooling. You’ll also contribute to shaping the frameworks, infrastructure, and shared tooling that enable safe, responsible, and efficient AI experimentation and deployment.

If you’re passionate about problem-solving, applying ML to real-world challenges, and bringing ideas to life in production environments, this role is a great fit.

What You’ll Be Doing

Machine Learning Engineering

• Build, deploy, and maintain ML models as services, streaming applications, or batch jobs across real-time and offline platforms.
• Develop scalable model APIs with strong CI/CD and observability practices.
• Implement model testing, monitoring, and rollback capabilities in production environments.
• Collaborate with Data Scientists to translate prototypes into reliable, maintainable ML applications.
• Identify opportunities to develop new ML solutions in partnership with Data Science teams.

Platform & Tooling

• Automate and standardise ML infrastructure using Docker, Kubernetes, and Terraform.
• Support and develop monitoring dashboards for key ML and AI services.
• Ensure cloud-native, secure, and cost-efficient deployments in AWS environments.
• Contribute to the development of shared platforms and tooling that enable model deployment and experimentation.

Compliance

• Adhere to governance, risk, and compliance obligations relevant to the role.
• Identify and escalate non-compliance issues when necessary.
• Proactively challenge processes that may impact compliance standards.
• Complete all mandatory compliance training and engage with compliance teams for clarification when needed.

What You’ll Bring

• 3–5 years of experience in machine learning engineering and data science.
• Advanced degree (PhD or Master’s) in a numerate discipline.
• Excellent programming skills in Java and Python for production systems.
• Strong foundations in machine learning and data science.
• Experience deploying ML models as APIs, batch jobs, or streaming services (e.g., Kafka Streams).
• Proficiency in containerised application deployment with Kubernetes.
• Demonstrated experience building ML solutions from concept to delivery.
• Strong cloud engineering skills (AWS preferred; Terraform or CloudFormation a plus).
• Excellent communication and collaboration skills.
• Up-to-date knowledge of modern ML and AI developments.

Why Join

This is a chance to work on impactful machine learning use cases that shape the future of customer experiences and business operations. You’ll be part of a collaborative environment where innovation is encouraged, and you’ll have the autonomy to influence tooling, frameworks, and production ML strategy.

Please note: Sponsorship is not offered for this position. Candidates must have the existing right to work in the relevant location.