We are seeking a talented Machine Learning Engineer to design and build advanced machine learning & AI solutions that enhance our ability to detect financial crime, prevent fraud, and safeguard our customers.
Working closely with data scientists and engineers, you will focus on developing scalable ML pipelines, building production‑ready model code, and supporting robust monitoring systems. You will contribute across the full machine learning lifecycle, from initial concept and data exploration through to deployment, while ensuring adherence to governance, documentation, and regulatory standards.
To be successful as a Machine Learning Engineer, you should have:
- Proven experience in machine learning development, including training and/or deploying models in production environments.
- Strong programming skills in Python.
- Experience working with distributed data processing frameworks (e.g., Spark).
- Familiarity with deep learning or NLP frameworks (e.g., PyTorch, Hugging Face).
- Good understanding of software engineering principles and machine learning lifecycle practices (MLOps/LLMOps).
- Experience contributing to ML projects and working with cross‑functional stakeholders.
Some Other Highly Valued Skills May Include
- Exposure to LLMs and agentic AI systems, or an interest in developing expertise in this area (e.g., LangGraph, CrewAI, AWS Bedrock, Anthropic SDKs).
- Experience with cloud platforms (AWS, Azure, or GCP) or ML platforms (e.g., Databricks).
- Exposure to fraud detection, anti‑money laundering, anomaly detection, or graph‑based modelling.
- Understanding of model governance, risk management, and regulatory considerations in financial services.
Role located at our Glasgow office.
Purpose of the Role
To design, develop, implement, and support mathematical, statistical, and machine learning models and analytics used in business decision‑making.
Accountabilities
- Design analytics and modelling solutions to complex business problems using domain expertise.
- Collaborate with technology to specify any dependencies required for analytical solutions, such as data, development environments and tools.
- Develop high performing, comprehensively documented analytics and modelling solutions, demonstrating their efficacy to business users and independent validation teams.
- Implement analytics and models in accurate, stable, well‑tested software and work with technology to operationalise them.
- Provide ongoing support for the continued effectiveness of analytics and modelling solutions to users.
- Demonstrate conformance to all Barclays Enterprise Risk Management Policies, particularly Model Risk Policy.
- Ensure all development activities are undertaken within the defined control environment.
Assistant Vice President Expectations
- To advise and influence decision making, contribute to policy development and take responsibility for operational effectiveness. Collaborate closely with other functions/ business divisions.
- Lead a team performing complex tasks, using well developed professional knowledge and skills to deliver on work that impacts the whole business function. Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives and determination of reward outcomes.
- For an individual contributor, guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. Identify new directions for assignments and/or projects, identifying a combination of cross‑functional methodologies or practices to meet required outcomes.
- Consult on complex issues; providing advice to People Leaders to support the resolution of escalated issues.
- Identify ways to mitigate risk and develop new policies/procedures in support of the control and governance agenda.
- Take ownership for managing risk and strengthening controls in relation to the work done.
- Perform work that is closely related to that of other areas, requiring understanding of how areas coordinate and contribute to the achievement of the organisation’s objectives.
- Collaborate with other areas of work, for business‑aligned support areas to keep up to speed with business activity and the business strategy.
- Engage in complex analysis of data from multiple sources of information, internal and external sources such as procedures and practices (in other areas, teams, companies, etc.) to solve problems creatively and effectively.
- Communicate complex information. ‘Complex’ information could include sensitive information or information that is difficult to communicate because of its content or audience.
- Influence or convince stakeholders to achieve outcomes.
All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave.
