Machine Learning Engineer (Leeds)

Machine Learning Engineer

Salary: £70,000

Location: Leeds or Manchester

This position is for an experienced Machine Learning Engineer to join a newly established data science team. The primary focus is on building and maintaining the infrastructure to support the full data science lifecycle from data ingestion to model deployment, monitoring, and upgrades within Azure and Databricks environments. The engineer will work closely with data scientists in a collaborative, cross-functional setting, helping transition models from research into production.

Key Responsibilities:

• Own and develop deployment frameworks for data science services.
• Ownership of the deployment framework for all data science services. You will have oversight of how data will flow into the data science life cycle from the wider business data warehouse.
• Oversight of the automation of the data science life cycle (dataset build, training, evaluation, deployment, monitoring) when we move to production.
• Automate the data science pipeline (data prep to deployment).
• Collaborate with cross-functional teams to ensure smooth productionization of models.
• Write clean, production-ready Python code.
• Apply software engineering best practices, CI/CD, TDD.

Required Skills:

• Proficiency in Python, Databricks, and Azure.
• Experience with deployment tools (e.g., AKS, managed endpoints).
• Strong software engineering background (CI/CD, VCS, TDD).
• Ability to integrate ML into business workflows.

Desirable:

• Background in quantitative disciplines (math, stats, physics).
• Experience in finance, insurance, or ecommerce.
• Familiarity with ML frameworks like TensorFlow, XGBoost, and SKLearn.

If this sounds like something you are interested in, please get in contact: thomas.deakin@spgresourcing.com SPG Resourcing is an equal opportunities employer and is committed to fostering an inclusive workplace which values and benefits from the diversity of the workforce we hire. We offer reasonable accommodation at every stage of the application and interview process.