Machine Learning Engineer

A European consultancy is seeking a Databricks-focused Machine Learning Engineer for a 12-24 month contract.

This role supports the full end-to-end model lifecycle in production environments built on Azure and Databricks, collaborating with business units, customer teams across 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 and act as a technical point of contact for onboarding to Databricks, model deployment and pipeline design.
• Leverage key Databricks features such as MLflow, Workflows, Unity Catalog, Model Serving and Monitoring to enable scalable and manageable solutions.
• Implement governance and observability, integrating compliance, monitoring and audit features across the full machine learning lifecycle.
• Lead efforts to move models into production, ensuring they are stable, secure and scalable.
• Work directly on model hosting, monitoring, drift detection and retraining processes.
• Collaborate with teams in customer‑facing meetings, workshops and solution design sessions across departments.
• Contribute to platform and knowledge improvement, supporting continuous development of Databricks platform services and promoting knowledge sharing across teams.

Essential Skills and Experience

• End‑to‑end ML/AI lifecycle expertise with hands‑on experience across data preparation, model development, deployment, monitoring and retraining.
• Proficiency with Azure Databricks and core components: MLflow, Delta Lake, Unity Catalog, Workflows, Model Serving.
• Experience with machine learning frameworks (PyTorch, TensorFlow, or Scikit‑learn).
• DevOps and automation knowledge: CI/CD pipelines, infrastructure‑as‑code (e.g., Terraform), container technologies like Docker.
• Cloud platform familiarity, preferably Azure, with openness to AWS or other providers.
• Strong stakeholder‑focused communication, explaining complex technical concepts clearly.
• Governance and compliance awareness, including explainability and security controls.
• Experience with large language models, generative AI or multimodal orchestration tools (optional but plus).
• 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 (plus).

If this sounds like an exciting opportunity, please apply with your CV.

Seniority level

• Mid‑Senior level

Employment type

• Contract

Job function

• Information Technology
• Business Consulting and Services