Hybrid MLOps Engineer: Build & Scale ML Pipelines

Co-operative Bank plc; Coventry; Hybrid; GBP 60,000 - 82,000; Full time

Benefits offered by this job

Car allowance

Discretionary bonus

Excellent benefits

Job summary

Coventry Building Society is hiring for a Data & Analytics Delivery role based in Coventry. The role involves designing, developing and testing ML and data engineering solutions, with support for deployment, monitoring and lifecycle management of models and data pipelines.

You will work in a team-led hybrid environment (2–3 days in the office), travel to other sites as needed and occasional out-of-hours work in line with overtime policy. Full-time, 35 hours per week.

Qualifications

• Strong Machine learning Operations and data engineering development experience.
• Experience with cloud data tooling (S3/Glue/Redshift/SageMaker) or equivalent.
• Databricks experience.
• Strong Data related programming skills SQL/Python/Spark.
• Experience of industrialising and scaling machine learning models.
• Experience in deployment, monitoring, retraining and lifecycle management of ML models.
• Understanding of containerisation and infrastructure tooling (e.g., Docker, Kubernetes).
• Exposure to MLOps practices such as model lifecycle management, monitoring, observability, and pipeline automation.
• Experience in Finance data context would be helpful.
• SAS and Informatica experience would help.
• Scala knowledge.
• Experience in an Agile team.
• Data modelling methods such as Kimball, Data Vault, Lakehouse.
• QA, testing automation experience.

Responsibilities

• Design, develop and test quality ML and data engineering solutions.
• Support development, deployment and monitoring of ML models and data pipelines.
• Mentor teammates on design, build and test activities.
• Collaborate in a team-led hybrid setup with travel for key meetings.

Job description

Coventry Building Society is hiring for a Data & Analytics Delivery role based in Coventry. The role involves designing, developing and testing ML and data engineering solutions, with support for deployment, monitoring and lifecycle management of models and data pipelines.

You will work in a team-led hybrid environment (2–3 days in the office), travel to other sites as needed and occasional out-of-hours work in line with overtime policy. Full-time, 35 hours per week.