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.