Machine Learning Engineer

Job Title: ML Engineer (Databricks) Rate: Depending on experience Location: Remote Contract Length: 12-24 months A European consultancy are seeking a Databricks focused Machine Learning Engineer to join the team on a long term 12-24 month contract. This role will be supporting the full end-to-end model lifecycle in production environments built on Azure and Databricks not only internally, but also in close collaboration with business units and customer teams across a international business units. Databricks expertise is a must. Core Responsibilities • Build and manage ML/MLOps pipelines using Databricks • Design, optimise and operate robust end-to-end machine learning pipelines within the Databricks environment on Azure. • Support internal project teams • Act as a technical point of contact for internal stakeholders, assisting with onboarding to Databricks, model deployment and pipeline design. • Leverage key Databricks features • Utilise capabilities such as MLflow, Workflows, Unity Catalog, Model Serving and Monitoring to enable scalable and manageable solutions. • Implement governance and observability • Integrate compliance, monitoring and audit features across the full machine learning lifecycle. • Operationalise ML/AI models • Lead efforts to move models into production, ensuring they are stable, secure and scalable. • Hands-on with model operations • Work directly on model hosting, monitoring, drift detection and retraining processes. • Collaborate with internal teams • Participate in customer-facing meetings, workshops and solution design sessions across departments. • Contribute to platform and knowledge improvement • Support the continuous development of Databricks platform services and promote knowledge sharing across teams. Essential Skills and Experience: • End-to-end ML/AI lifecycle expertise • Strong hands-on experience across the full machine learning lifecycle, from data preparation and model development to deployment, monitoring, and retraining. • Proficiency with Azure Databricks • Practical experience using key components such as: • MLflow for experiment tracking and model management • Delta Lake for data versioning and reliability • Unity Catalog for access control and data governance • Workflows for pipeline orchestration • Model Serving and automation of the model lifecycle • Machine learning frameworks • Working knowledge of at least one widely used ML library, such as PyTorch, TensorFlow, or Scikit-learn. • DevOps and automation tooling • Experience with CI/CD pipelines, infrastructure-as-code (e.g., Terraform), and container technologies like Docker. • Cloud platform familiarity • Experience working on Azure is preferred; however, a background in AWS or other providers with a willingness to transition is also suitable. • Production-grade pipeline design • Proven ability to design, deploy, and maintain machine learning pipelines in production environments. • Stakeholder-focused communication • Ability to explain complex technical concepts in a clear and business-relevant way, especially when working with internal customers and cross-functional teams. • Governance and compliance awareness • Exposure to model monitoring, data governance, and regulatory considerations such as explainability and security controls. • Agile working practices • Comfortable contributing within agile teams and using tools like Jira or equivalent project management platforms. Desirable Experience • Experience working with large language models (LLMs), generative AI or multimodal orchestration tools • Familiarity with explainability libraries such as SHAP or LIME • Previous use of Azure services such as Azure Data Factory, Synapse Analytics or Azure DevOps • Background in regulated industries such as insurance, financial services or healthcare If this sounds like an exciting opportunity please apply with your CV.