

AI/Data Scientist with expertise in Generative AI, NLP, and machine learning, experienced in building end-to-end intelligent systems from modeling to production. Proven track record in developing scalable solutions, including retrieval-augmented generation, semantic search, and LLM evaluation frameworks, improving model accuracy and user experience. Combines deep AI/ML knowledge with full-stack engineering experience to deliver robust, production-ready systems with measurable business impact.
Machine Learning & AI:
Supervised Learning, Unsupervised Learning, Feature Engineering, Hyperparameter Tuning, Ensemble Learning, Gradient Boosting, Statistical Testing
Deep Learning & NLP:
Neural Networks, CNNs, NLP, Text Processing, Topic Modeling, Transformers, Embeddings, Semantic Search
Generative AI & LLMs:
Generative AI, RAG, Multimodal RAG, Hybrid Search, Vector Search, Re-ranking, Cross-Encoder, Bi-Encoder, LLM Evaluation, Multi-Agent Systems
Data & Vector Systems:
PostgreSQL, SQL Server, MongoDB, PGVector, Qdrant, Milvus
Backend & Distributed Systems:
Python, NET Core, FastAPI, REST APIs, GraphQL, Kafka, Redis, Distributed Systems
MLOps & Tools:
Docker, Kubernetes, CI/CD, Splunk, Dynatrace, PyTorch, TensorFlow, Scikit-learn, Git, Agile, Kanban