Data Scientist

Advanced Python development for ML/AI workloads

End-to-end ML lifecycle: model training, evaluation, fine-tuning, and labeling/tagging workflows

Generative AI systems design, including LLM-based application development

Prompt engineering optimization for large language models

Document AI pipelines: OCR/extraction, parsing, normalization, and text chunking for structured & unstructured data

Embedding generation pipelines for semantic search and retrieval

Vector similarity search implementation using vector databases

ML model integration with Vector DBs and MongoDB

Production-grade ML engineering: scalable, maintainable, and deployment-ready code