Drivers of change, it’s your time to pave new ways. Intellias, a leading software provider in the automotive industry, invites you to develop the future of driving. Join the team and create products used by 2 billion people in the world.

We are searching for an ML Engineer that can support our Data Science team on an interim basis developing, implementing and maintaining AI/ML solutions. The work will have a clear focus on defining technical requirements and implementing those solutions at scale, as well as implementing data pipelines for feature engineering. A close collaboration with Data Scientists and Data Engineers ensures that the Business Requirements are understood, and the solution is connected to the data flow.

Team Information:

We are a team of 4 Data Scientists working on solutions for the business. Among others, we developed algorithms for predicting the probability of default of our customers; churn; fraud; cross-and-upselling potential. We are working agile (2week sprints) and enjoy discussions over the topics at hand. Each Data Scientist is currently responsible for different projects and products. The ML Engineer will be required to support multiple projects/products and work with the entire team.

Requirements:

• End-to-End ML Lifecycle: Proven experience designing and implementing production-ready ML solutions, including data preprocessing, feature engineering, model training, and automated retraining loops.
• Cloud & Deployment: Deep expertise in Azure ML Studio for scaling model deployments and managing environments.
• Data Engineering for ML: Strong proficiency in Snowflake and dbt for building robust data pipelines and feature sets.
• Technical Stack: Advanced Python (specifically for ML frameworks) and SQL.
• DevOps Integration: Solid understanding of CI/CD principles and Git for collaborative code management within an MLOps framework.
• Collaborative Architect: Ability to translate business needs into technical architectures, working closely with Data Scientists and Data Engineers to ensure model impact.

Nice-to-Have:

• Foundational understanding of network security and cloud infrastructure (VNETs, Private Endpoints).
• Experience with API development (FastAPI/Flask) for model consumption.

Responsibilities:

• Implementing and maintaining AI/ML solutions.
• Defining technical requirements and implementing those solutions.
• Implementing data pipelines for feature engineering.
• Close collaboration with Data Scientists and Data Engineers.
• Ensures that the Business Requirements are understood and the solution is connected to the data flow.
• The focus is on defining technical requirements, implementing solutions at scale, and building feature engineering pipelines.
• You will ensure that model outputs are seamlessly integrated into the data flow and meet business requirements.