ML Engineer

We are currently looking for a Machine Learning Engineer to join our client's data team. This is a hands-on role where you'll design and build robust data pipelines, transform ML prototypes into production-ready systems, and champion MLOps best practices across the business. As a Machine Learning Engineer, you'll play a crucial role in ensuring our clients' data and AI strategy scales effectively, directly influencing the way millions of people engage with their products every day., This is a unique chance to combine data engineering with machine learning in a high-impact environment. You'll work closely with analysts, data engineers and stakeholders, ensuring models are reliable, scalable, and production-ready. Unlike many roles in the tech sector, this Machine Learning Engineer role gives you the visibility of seeing your work applied at scale, powering decision-making and user experiences for a vast audience.

Your day-to-day will include:

• Building and maintaining end-to-end data pipelines and feature engineering workflows.
• Deploying and monitoring ML models in production using tools such as MLflow, Vertex AI, or Azure ML.
• Driving best practices in MLOps, including CI/CD, experiment tracking, and model governance.
• Supporting the data warehouse and ensuring data quality, governance, and accessibility.
• Collaborating with cross-functional teams to deliver trusted datasets and insights.

Do you have experience in Spark?, * Degree in Computer Science, Engineering, Mathematics, or a related field.

• Proven experience in data or ML engineering.
• Strong knowledge of Python and SQL.
• Hands-on experience with cloud platforms (GCP or Azure) and Databricks.
• Familiarity with deploying ML workflows using MLflow, Vertex AI, or Azure ML.

Nice-to-have:

• Experience with Spark, CI/CD pipelines, and orchestration tools.
• Knowledge of Elasticsearch or digital/web analytics platforms.
• Understanding of the full machine learning lifecycle, from experimentation to evaluation.

• Competitive salary with annual reviews.
• Hybrid working model offering flexibility.
• Generous holiday allowance that increases with service.
• Onsite wellness facilities, subsidised meals, and gym access.
• Access to wellbeing support services and employee assistance programmes.
• Clear career progression and opportunities to work with cutting-edge tech.