Senior Data Scientist / Applied ML Engineer

Role purpose

As a Senior Data Scientist / Applied ML Engineer, you combine deep theoretical expertise in machine learning with extensive hands-on experience delivering production-grade AI solutions. 

Your impact goes far beyond building and fine-tuning models in notebooks, you are passionate about bringing AI systems into production and ensuring they deliver real business value.

You think holistically about the entire machine learning lifecycle, from feature engineering to model training, evaluation, deployment, serving, monitoring, continuous retraining, and redeployment. You understand the engineering challenges of operating AI systems at scale and can design robust, maintainable, and highly performance ML pipelines. 

Your strong theoretical foundation enables you to develop, evaluate, and optimize machine learning models across structured, visual, and textual data. 

Responsibility:-  

•  Think critically about how to validate hypotheses and measure model performance. 

• Embrace rapid iteration

•Quickly turn ideas into working prototypes, deploy them, validate them in production, learn from real-world feedback, and continuously improve them. 

•Do this in close collaboration with a Data Engineering team in days not months 

• Take ownership and show initiative in coming up with ideas and deriving experiments • You are an excellent communicator and collaborator. 

• Enjoy working in cross-functional teams alongside Data Engineers, Software Engineers, and domain experts. 

Requirement : 

• have at least 5 years of professional experience as a Data Scientist with strong AI engineering skills or as an ML/AI Engineer with a solid theoretical foundation in machine learning. 

•  highly proficient in Python and have experience building production-grade applications and APIs using frameworks such as FastAPI or Django.

• have hands-on experience developing machine learning models using libraries such as scikit-learn, PyTorch, NumPy, and Pandas.

 • have experience with at least one modern MLOps platform such as Kubeflow, MLflow, or KServe. 

• Experience working with PostgreSQL and Qdrant or other vector databases is a plus.

• Experience with Kubernetes and containerized ML workloads is considered a strong advantage. 

• Experience with geospatial data processing is highly desirable, including PostGIS database and libraries such as GeoPandas. 

• Fluent in English. German language skills are a plus.