Machine Learning Engineer (W/M/D)
Tasks
- Analyze large datasets identify modeling challenges propose solutions iterate
- Apply ML LLMOps practices model versioning production monitoring reproducibility continuous improvement
- Build data and ML pipelines
- Collaborate with data, product, and engineering teams
- Design end to end ML and AI pipelines
- Develop APIs and microservices for ML AI models
- Develop train evaluate deploy ML models
- Implement LLM RAG structured prompting few shot learning OCR integration
- Implement testing CI CD deployment automation
Perks/Benefits
- Additional paid days off
- Flexible work environment
- Free yoga lessons
- Health insurance
- Home office equipment support
- Lunch vouchers
- No meeting Wednesday mornings
- Paid child sick days
- Public transport reimbursement
- Remote-friendly
- Therapy coaching sessions
- Training budget
Skills/Tech-stack
AWS SageMaker | Artificial Intelligence | Azure Machine Learning | CI/CD | DVC | DeepEval | Docker | Docker containers | Dspy | Feature Engineering | Few-Shot Learning | Few-shot | Google Cloud | Google Cloud Vertex | Google Cloud Vertex AI | HuggingFace Transformers | Kubeflow | Kubernetes | Language Models | Language Processing | Large Language Models | LightGBM | MLflow | Machine Learning | Microservices | Natural Language | Natural Language Processing | OCR | Python | RAG | REST APIs | Retrieval-Augmented Generation | SQL | Scikit-learn | Structured prompting | Vertex AI | XGBoost
Education
Bachelor of Science | Master of Engineering | Master of Science
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