Bangalore, Karnataka, India
Summary
Led the end-to-end development and deployment of advanced Machine Learning and Generative AI solutions, optimizing model performance and integrating AI into enterprise workflows.
Highlights
Pioneered the research, development, and fine-tuning of advanced ML and Generative AI models, including LLMs (Gemini, GPTs), enhancing model behavior and contextual accuracy through prompt engineering and RAG techniques.
Engineered and integrated LLM-powered agents and multi-agent systems into enterprise workflows via RESTful APIs and WebSocket interfaces, facilitating task delegation, contextual search, and decision-making.
Designed and implemented robust ETL pipelines for collecting, preprocessing, and structuring large datasets (text, images, audio), ensuring high data quality, consistency, and bias mitigation for model training.
Deployed scalable ML/Gen AI models to AWS and GCP, optimizing inference speed and reducing computational costs through quantization and distillation techniques, while implementing real-time and batch processing.
Monitored model drift, hallucinations, and toxicity using custom evaluation metrics and MLOps tools like Kubeflow, establishing automated retraining pipelines and feedback loops for continuous improvement and alignment.
Developed RESTful APIs and integrated AI models with enterprise applications and chatbots, leveraging vector databases (Pinecone, FAISS, ChromaDB) for efficient retrieval-based AI solutions.