Machine Learning Engineer (W/M/D)
Tasks
- Analyze large datasets and iterate on solutions
- Apply ML and LLM research to real world use cases
- Apply few shot learning
- Build APIs and microservices for ML models
- Build and maintain data and ML pipelines
- Collaborate with data, product, and engineering teams
- Design and deploy end to end machine learning pipelines
- Implement LLM-powered features
- Implement ML and LLM Ops practices
- Implement RAG and structured prompting
- Implement testing CI CD and deployment automation
- Integrate OCR for information extraction
- Manage model versioning and reproducibility
- Monitor models in production
- Train evaluate and deploy machine learning models
Perks/Benefits
- Additional paid time off
- Employee feedback survey
- Family care policy
- Flexible work environment
- Health insurance
- Holiday bonus
- Home office equipment support
- Lunch vouchers
- Moka care mental health support
- No meeting Wednesday mornings
- Public transport reimbursement
- Remote-friendly
- Training budget
- Work-life initiatives
Skills/Tech-stack
AWS SageMaker | Artificial Intelligence | Azure Machine Learning | DVC | Data Preprocessing | DeepEval | Docker | Dspy | Feature Engineering | Few-Shot Learning | Few-shot | Google Cloud | Google Cloud Vertex | Google Cloud Vertex AI | HuggingFace Transformers | Kubeflow | Kubernetes | LLM | LightGBM | MLflow | Machine Learning | Microservices | OCR | Python | RAG | REST API | SQL | Scikit-learn | Structured prompting | Vertex AI | XGBoost
Education
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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