{ “@context”: “http://schema.org”, “@type”: “JobPosting”, “title”: “Machine Learning Engineer”, “description”: “
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
Company: X4 Technology
Location:
Job Description:
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
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Posted: April 11th, 2026