About This Role
Making machine learning models ready for production use is the core of this role, together with deploying, monitoring and scaling them. The Machine Learning Engineer will work at the intersection of software engineering, MLOps, and data science, building frameworks, pipelines, and services. The role suits an engineer who enjoys solving complex challenges and seeing their work underpin real-world AI and machine learning applications.
Responsibilities
- Partner with Data Scientists to transition models from experimentation into scalable, production-ready applications and services
- Design, build, and maintain MLOps frameworks, deployment pipelines, and automated model lifecycle processes
- Develop and support CI/CD pipelines, version control frameworks, and automated testing processes for machine learning solutions
- Build APIs, services, and integration patterns that enable machine learning models to be embedded into business systems and customer-facing applications
- Implement model monitoring, performance tracking, drift detection, and automated retraining capabilities to ensure ongoing reliability and business value
- Work with cloud-native technologies including Azure Databricks, MLflow, and related platforms to support enterprise-scale machine learning workloads
- Collaborate with Data Engineers, Architects, and Developers to deliver secure, scalable, and maintainable machine learning solutions
Requirements
- Proven experience deploying machine learning solutions into production environments and supporting them throughout their lifecycle
- Strong understanding of version control practices, including Git, branching strategies, and release management
- Strong software engineering skills in Python, with a solid understanding of engineering best practices, testing, code quality, and maintainability
- Experience building CI/CD pipelines, MLOps frameworks, and automated machine learning deployment processes
- Hands-on experience with Azure Databricks or similar machine learning platforms (e.g., MLFlow, KubeFlow, SageMaker etc)
- Experience building APIs, microservices, and cloud-based solutions that integrate machine learning capabilities into wider platforms and applications
- Excellent collaboration and technical communication skills, with the ability to work effectively alongside Data Scientists, Architects, Engineers, and Business Analysts / Project Managers
Benefits
- Annual discretionary bonus scheme
- Pension
- Life assurance
- 25 days annual leave plus bank holidays
- Enhanced family leave
- Commitment and dedication to your ongoing personal and professional development
- Access to a wealth of employee discounts across the Halfords suite of products and services
