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MLOps Engineer
We are looking for a highly skilled Data Scientist / MLOps Engineer with deep expertise in Python, Databricks, and Azure DevOps, to strengthen our Marine‑Operations Performance Monitoring & Forecasting capabilities. This role combines advanced data science, machine learning engineering, and cloud‑based MLOps to deliver high‑quality predictive models and scalable automation for operational and business planning teams.
Key Responsibilities
Performance Monitoring & Forecasting
- Enhance and refactor code for a dynamic, two‑step regression model used to forecast performance of shipboard systems (HVAC, Service Power, etc.).
- Develop an improved Fuel Forecasting Model incorporating additional features (fuel mix, speed, weather) and deploy it via Container Apps or API endpoints for planning simulations.
Data Science & Machine Learning
- Build predictive models using Python and PySpark, ensuring high accuracy, scalability, and robustness.
- Perform feature engineering, feature selection, and data profiling to improve model performance.
- Apply ML algorithms for regression, classification, and clustering using frameworks like TensorFlow, PyTorch, or equivalent.
- Create and maintain Python machine‑learning libraries and reusable modules.
MLOps & Azure DevOps
- Enhance the Azure DevOps/MLOps ecosystem for model development, deployment, versioning, and monitoring.
- Implement best practices for CI/CD, automated testing, environment management, and secure deployment pipelines.
- Use MLflow for experiment tracking, model registry, and reproducibility.
- Integrate Azure services such as Azure ML, ADF, Azure Functions, Cosmos DB, and others into ML workflows.
- Ensure strong governance through Azure security management, identity control (AAD), and secure networking.
Engineering & Operational Excellence
- Write efficient, scalable, production‑grade Python code for data processing, model training, and model serving.
- Support system reliability, scalability, and performance for pricing, inventory automation, and forecasting tools.
- Collaborate with cross‑functional teams (engineering, analytics, operations) to align models with operational needs.
Required Skills & Qualifications
- Expert Python software engineering skills (most important).
- Hands‑on experience with PySpark, MLflow, and Python package management.
- Proficiency with Databricks for large‑scale data processing and modeling.
- Experience with Azure DevOps, CI/CD pipelines, and MLOps principles.
- Familiarity with cloud‑based ML environments (ideally Azure ML).
- Strong understanding of ML algorithms, model development, and feature engineering.
- Ability to write maintainable, scalable code for ML pipelines and deployments.
- Strong analytical skills for data profiling and model training.
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Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organizations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2024 global revenues of €22.1 billion.
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