Data

"Enhance an AI SaaS solution!"

We are building a real-time data platform. As a member of our team, you will play a pivotal role in developing and maintaining the infrastructure that powers our ML solutions.

A significant challenge we face is onboarding a large number of customers onto our platform, where automation and repeatability are crucial for success. This role combines the best of data engineering with the demands of machine learning operations, ensuring the seamless operation of data pipelines and ML models from development to production.

The ideal candidate is an innovative problem-solver who is passionate about both data engineering and MLOps. You will thrive in our dynamic startup environment, wearing multiple hats when required to meet our evolving needs. Your ability to collaborate across teams, automate processes, and ensure repeatability will be key to driving our data and ML infrastructure forward.

What you’d be working on

• Design, develop, and manage scalable and robust data pipelines that support machine learning models in production.

• Implement and maintain CI/CD pipelines for data and ML workflows, ensuring smooth transitions from development to production.

• Automate the onboarding process for new customers, ensuring scalability and repeatability across deployments.

• Collaborate with data scientists and AI Engineers to optimize and automate data processing and model deployment.

• Ensure the seamless integration of ML models with production environments, enabling real-time and batch inference capabilities.

• Develop and maintain tools and frameworks for automated data management, model retraining, and monitoring.

• Utilize MLFlow for model tracking, versioning, and lifecycle management.

• Implement model serving solutions to deploy and manage real-time inference pipelines.

What you bring along

• 3+ years of experience in data engineering, ML Engineering or a related field, with a strong focus on MLOps.

• Experience in building and managing data pipelines that feed into machine learning models.

• Very good knowledge of Python; experience with PySpark is appreciated.

• Hands-on experience with data engineering tools and platforms; a background in Databricks is highly valued.

• Experience with MLFlow for model tracking and lifecycle management.

• Familiarity with model serving technologies and deploying models for real-time inference.

• Familiarity with CI/CD tools and processes, including Jenkins, Git, or similar.

• Ability to wear multiple hats and thrive in a startup environment where flexibility and initiative are key.

• A mindset that embraces continuous improvement, automation, and scalability, particularly in customer onboarding and deployment processes.

Sound interesting? Contact us today to discuss the opportunity further.