Machine Learning Engineer in London

Join us as a Machine Learning Engineer

You’ll lead, drive, and embed the deployment, automation, maintenance, and monitoring of machine learning models and algorithms to ensure they work effectively in a production environment.

You’ll understand the complex requirements and needs of business stakeholders, developing strong relationships and demonstrating how machine learning solutions can meet those needs to achieve business strategy goals.

This is a high‑profile role that offers considerable exposure and the opportunity to make a significant impact.

You will work from home some of the time, and also spend a minimum of two days per week working from the office.

Responsibilities

• Work collaboratively with colleagues to production‑ise machine learning models, including pipeline design, development, testing, and deployment, ensuring that the original intent and knowledge are carried over to production.
• Create frameworks to ensure robust monitoring of machine learning models within a production environment, making sure these models deliver expected quality and performance.
• Identify and communicate the actions needed to implement the function's strategy and business plan within the business area and explain the relationship to the broader organization’s mission, vision, and values, followed by motivating people to achieve local business goals.
• Collaborate with colleagues to design and develop advanced machine learning products.
• Codify and automate complex machine learning model production, including pipeline optimisation, tuning, and fault finding.
• Lead both direct reports and wider teams in an agile way within multi‑disciplinary data and analytics teams to achieve agreed project and scrum outcomes.
• Transform advanced data science prototypes and apply appropriate machine learning algorithms and tools.
• Provide technical guidance and input on the design and implementation of machine learning algorithms and models.
• Lead and deliver stakeholder engagement activities to develop effective project working relationships, ensuring stakeholder needs and concerns are identified and met.

Qualifications

• Strong academic background in a STEM discipline such as Mathematics, Physics, Engineering, or Computer Science.
• Extensive experience building, testing, supporting, and deploying advanced machine learning models into a production environment using modern CI/CD tools such as Git, Team City, and Code Deploy.
• Financial Services knowledge, and the ability to identify wider business impact, risk, and opportunities, making connections across key outputs and processes.
• Strong communication skills with the ability to proactively engage with a wide range of stakeholders.
• Strong understanding of fraud and financial crime systems, including transaction monitoring, onboarding/KYC processes, and real‑time risk decisioning, with the ability to apply this knowledge to analytical and machine learning solutions.
• The ability to use data to solve business problems from hypotheses through to resolution.
• Extensive experience with machine learning on large datasets.
• A strong understanding of machine learning approaches and algorithms.
• Experience using programming and scripting languages such as Python and Bash.
• Experience in synthesizing, translating, and visualizing data and insights for key stakeholders.
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