GIOS Technology
Machine Learning Engineer (ML Infrastructure/Databricks/MLOps/Airflow/Insurance)
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer with expertise in ML infrastructure, Databricks, MLOps, and Airflow, requiring experience in the insurance industry. It offers a hybrid work location in London, a contract length of "unknown," and a competitive pay rate.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
-
💰 - Day rate
Unknown
-
🗓️ - Date
March 13, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
Hybrid
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
London Area, United Kingdom
-
🧠 - Skills detailed
#Airflow #Deployment #Python #Databricks #Data Engineering #Model Deployment #MLflow #Data Science #Apache Airflow #ML (Machine Learning) #Scala #Monitoring
Role description
I am hiring for Machine Learning Engineer (ML infrastructure/Databricks/MLOps/Airflow/Insurance)
Location: London - 2 Days onsite weekly
Job Description
• Design and implement scalable machine learning infrastructure on the Databricks Lakehouse platform.
• Build and manage end-to-end MLOps pipelines including data preparation, model training, deployment, and retraining.
• Collaborate with data scientists, engineers, and product teams to optimize machine learning workflows.
• Implement experiment tracking, model registry, and model deployment using MLflow.
• Automate testing, validation, and monitoring of ML models using Databricks tools and monitoring platforms.
• Orchestrate machine learning workflows and pipelines using Airflow and Databricks environments.
Key Skills
Python, Machine Learning, MLOps, Databricks, MLflow, Apache Airflow, Data Engineering, Lakehouse Architecture, Model Deployment, CI/CD
I am hiring for Machine Learning Engineer (ML infrastructure/Databricks/MLOps/Airflow/Insurance)
Location: London - 2 Days onsite weekly
Job Description
• Design and implement scalable machine learning infrastructure on the Databricks Lakehouse platform.
• Build and manage end-to-end MLOps pipelines including data preparation, model training, deployment, and retraining.
• Collaborate with data scientists, engineers, and product teams to optimize machine learning workflows.
• Implement experiment tracking, model registry, and model deployment using MLflow.
• Automate testing, validation, and monitoring of ML models using Databricks tools and monitoring platforms.
• Orchestrate machine learning workflows and pipelines using Airflow and Databricks environments.
Key Skills
Python, Machine Learning, MLOps, Databricks, MLflow, Apache Airflow, Data Engineering, Lakehouse Architecture, Model Deployment, CI/CD
