At a Glance
- Tasks: Build and deploy cutting-edge machine learning solutions on Azure and Databricks.
- Company: Join a forward-thinking tech company focused on innovation and collaboration.
- Benefits: Competitive pay, flexible working options, and opportunities for professional growth.
- Other info: Dynamic role with potential for career advancement in a thriving environment.
- Why this job: Make a real impact by operationalising ML models and enhancing data pipelines.
- Qualifications: Experience in ML engineering, Python, and collaborative teamwork is essential.
The predicted salary is between 60000 - 80000 £ per year.
Employment Type
6-Month Fixed-Term Contract / Contract Inside IR35
Start Date
Immediate
We are seeking a Machine Learning / MLOps Engineer to help build, deploy, and support production-ready machine learning solutions on Azure and Databricks.
Working closely with Data Scientists, Data Engineers, Platform Engineers, and business stakeholders, you will be responsible for operationalising ML models, building scalable data and ML pipelines, implementing monitoring, and supporting the end-to-end ML lifecycle.
This role will initially span MLOps, data engineering, and platform activities while the capability continues to mature.
Key Responsibilities
- Deploy and operationalise machine learning models developed by Data Science teams.
- Build and maintain ML and data pipelines using Python, Py Spark, SQL, Azure, and Databricks.
- Develop and manage Databricks Workflows, Jobs, MLflow, and model deployment processes.
- Implement CI/CD pipelines and Git-based development practices.
- Build monitoring and ingest for model performance, data quality, workflow failures, and operational health.
- Manage model lifecycle activities including versioning, deployment, testing, and continuous improvement.
- Collaborate with platform, cloud, Dev Ops, security, and operational teams to ensure scalable and secure deployments.
- Create deployment documentation, runbooks, and support processes.
- Hands‑on experience as an ML Engineer, MLOps Engineer, or similar role.
- Strong experience with
- Databricks
- Python, Py Spark, SQL
- MLflow and Databricks Workflows
- CI/CD and Git
- Machine Learning deployment and operational support
- Experience building and maintaining production‑grade ML pipelines.
- Understanding of model monitoring, observability, testing, and governance.
- Experience working across Data Science, Engineering, and Platform teams.
- Strong troubleshooting, communication, and stakeholder management skills.
- Unity Catalog and Databricks Model Registry.
- Terraform or Infrastructure‑as‑Code tools.
- Retail, forecasting, recommendation, or personalisation use cases.
- Azure or Databricks certifications.
Hurry & apply for a more detailed conversation!
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