Senior Machine Learning Engineer
Tasks
- Apply NLP techniques
- Build LLM orchestration pipelines
- Collaborate with data engineering teams
- Conduct load testing for SLAs
- Create documentation and demos
- Deploy machine learning services
- Design machine learning solutions
- Develop machine learning models
- Develop service APIs
- Evaluate NLP and RAG models
- Fine-tune and adapt LLMs
- Implement MLOps practices
- Implement RAG systems
- Mentor junior team members
- Review code for SOLID principles
Perks/Benefits
- Birthday off
- Holiday time after first anniversary
- Paid parental leave
- Paid time off
- Remote work flexibility
- Stock share scheme
Skills/Tech-stack
Agile | BERT | BM25 | CI/CD | Clustering | CrewAI | Data Augmentation | Data Cleansing | Data Preprocessing | Decision Trees | Dense retrieval | Dimensionality Reduction | Docker | Entity recognition | Event Sourcing | FastAPI | Fine Tuning | Flask | GPT | Git | Hugging Face | Hugging Face Transformers | Hybrid retrieval | Information Extraction | K-Means | KNN | Kafka | Kanban | Keras | Knowledge graphs | Kubernetes | Langchain | Langgraph | Language Models | Language Processing | Large Language Models | MLOps | Machine Learning | Model Deployment | Model Evaluation | Named Entity Recognition | Natural Language | Natural Language Processing | Naïve Bayes | NumPy | Pandas | Prompt engineering | PyTorch | Python | Question Answering | RAG | RabbitMQ | Random Forest | Retrieval strategies | SQL | SVM | Scikit-learn | Scrum | Semantic Search | Sentiment Analysis | Similarity Search | Summarization | T5 | TensorFlow | Text Classification | Transformers | Vector Databases | Vector Search | Vector embeddings
Education
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