Senior Machine Learning Engineer

Overview

In this role you will design, build and maintain production-grade machine learning solutions within the Consumer Data Science team for Lloyds Banking Group. You will work closely with data scientists and engineers to scale ML across the business, driving production readiness and governance. The role blends software engineering with ML, delivering impactful customer outcomes at-scale. You will be part of a diverse, collaborative environment that values learning and innovation, with opportunities to influence platform choices and improve systems.

Pay / Benefits

• generous pension contribution up to 15%
• annual performance-related bonus
• share schemes including free shares
• discounted shopping
• 30 days’ holiday, with bank holidays on top
• wellbeing initiatives and parental leave policies

Responsibilities

• Develop and maintain ML systems in Python with data scientists and other engineers.
• Take projects from experimentation to production, integrating with CI/CD, APIs, and Dynatrace.
• Collaborate across business and technical teams to understand requirements and influence decisions.
• Propose and advocate for technology, platform, and architecture choices per guidelines.
• Drive engineering standards and knowledge sharing across the team.
• Lead incident management and resolution with the platform team and business partners.
• Identify opportunities to improve solutions and present clear delivery plans.
• Ensure results align with data science governance and risk management policies.

Key requirements

• Advanced understanding of software engineering, automated testing, CI/CD, and production deployments.
• Commercial experience with Python ML libraries (Pandas, Scikit-learn, XGBoost).
• Experience with cloud services and data stores (Vertex AI, BigQuery, Cloud Run).
• Hands-on with MLOps tooling for large datasets (Kubeflow Pipelines, dbt) and production monitoring.
• Infrastructure as Code experience (Terraform), Kubernetes, API authentication flows, and networking.
• Experience collaborating with central platform, architecture and governance teams to move solutions to live production.
• Experience across the full SDLC from experimentation to live production.
• Understanding of retail banking or willingness to apply technical skills in this area.

• driven
• inquisitive
• eager to learn
• Pandas
• Scikit-learn
• XGBoost

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