Description:
- Core ML & Stats (optimization, supervised/unsupervised learning)
- NLP (semantic search, embeddings, text modeling)
- MLOps (MLflow, Kubeflow, Airflow, Docker, CI/CD)
- Proficiency in Python, PyTorch/TensorFlow, Hugging Face, LangChain, and cloud platforms.
- Ability to translate complex ideas into scalable, real world systems.
- Knowledge Graphs (RDF/OWL, Neo4j, graph ML)
- LLMs & Transformers (fine tuning, RAG, prompt engineering). |
