AI/ML Engineer with German language proficiency

Developing and delivering parts of a product, in accordance to the customers’ requirements and organizational quality norms. Activities to be performed include:

• German language proficiency

• Requirement analysis and design of software solutions based on requirements and architectural /design guidelines. 

• Implementation of features and/or bug-fixing and delivering solutions in accordance with coding guidelines and on-time with high quality.

• Identification and implementation of unit and integration tests to ensure solution addresses customer requirements, and quality and security requirements of product are met.

• Performing code review and creation / support for relevant documentation (requirement/design/test specification).

• Ensuring integration and submission of solution into software configuration management system, within committed delivery timelines.

Performing regular technical coordination / review with stake holders and ensuring timely reporting and escalations if any.

Job requirements/ skills:

• Translate ambiguous business problems into analytical/ML approaches; define success metrics and experiment design (A/B, DoE).

• Explore & prepare data: profiling, feature engineering, handling bias/leakage, data quality checks.

• Build and validate models (supervised/unsupervised/time series/NLP/CV as relevant); compare baselines and SOTA methods.

• Ship insights and/or models: dashboards, notebooks, or production endpoints with proper monitoring.

• Communicate results clearly to technical and non-technical stakeholders, document assumptions and limitations.

• Contribute to data governance and reusable tooling (feature stores, evaluation frameworks).

Required qualifications

• 4–7 years (or strong internships) in data science/analytics/ML.

• Proficiency in Python (pandas, NumPy, scikit-learn); SQL fluency for large datasets.

• Solid statistics/experimental design (hypothesis testing, causal inference basics, confidence intervals).

• Experience building and validating models end-to-end; familiarity with model evaluation and error analysis.

• Strong storytelling: ability to translate findings into business recommendations.

Nice to have

• Experience with one of: NLP (spaCy, Hugging Face), CV (OpenCV, TorchVision), Recommenders, Time Series (Prophet, statsmodels).

• Familiarity with ML in production (FastAPI/Flask, model registries, feature stores).

• Cloud & data stack: AWS/GCP/Azure, Spark, dbt, Airflow; BI (Power BI/Tableau/Looker).

• Version control & workflows: git, CI/CD, experiment tracking (MLflow/Weights & Biases).