Machine Learning Engineer (W/M/D)
Tasks
- Analyze large datasets
- Apply best practices in testing
- Build data and ML pipelines for training and evaluation
- Collaborate with data, product, and engineering teams
- Continuously improve ML systems
- Design end-to-end machine learning pipelines
- Develop APIs and microservices for ML models
- Develop and deploy ML and AI models
- Develop few shot learning solutions
- Develop structured prompting
- Ensure ML reproducibility
- Identify modeling challenges
- Implement CI/CD and deployment automation
- Implement LLM-powered features
- Implement ML Ops model versioning
- Implement ML Ops production monitoring
- Implement RAG
- Integrate OCR for extraction
- Propose modeling solutions
- Stay current on machine learning and LLM research
- Transform prototypes into production solutions
Perks/Benefits
- Additional paid days off
- Annual learning budget
- Family care policy
- Flexible work schedule
- Free therapy or coaching sessions
- Health insurance
- Holiday bonus
- Home office equipment support
- Lunch vouchers
- No meeting Wednesday mornings
- Paid child sick days
- Public transport reimbursement
- RTT
- Remote-friendly work environment
- Workshops and manager coaching
Skills/Tech-stack
AWS SageMaker | Artificial Intelligence | Azure ML | CI/CD | DVC | Data Preprocessing | Docker | Dspy | Feature Engineering | Few-Shot Learning | Few-shot | Google Cloud | Google Cloud Vertex | Google Cloud Vertex AI | HuggingFace Transformers | Kubeflow | Kubernetes | LLM | Language Processing | LightGBM | MLflow | Machine Learning | Model versioning | Natural Language | Natural Language Processing | OCR | Production Monitoring | Python | RAG | Reproducibility | Retrieval-Augmented Generation | SQL | Scikit-learn | Structured prompting | Vertex AI | XGBoost
Education
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