Building the future with AI, one algorithm at a time
Engineering
Engineering
Infrastructure
AI Engineer with more than 2 years of experience in designing and implementing generative AI solutions, specializing in autonomous AI agents.
Proficient in Python, AWS, and vector databases, with a strong command of LangChain, RAG systems, and prompt engineering techniques. Demonstrated ability to create scalable ML/AI solutions from the ground up, while collaborating effectively with cross-functional teams to deliver high-quality software products.
Career aspirations include further enhancing technical expertise and contributing to innovative AI initiatives. Currently researching novel approaches to improve RAG retrieval accuracy by experimenting with SLM and LLMs combined with vector databases.
Building conversational AI and autonomous agent solutions using LLMs, RAG systems, and advanced prompt engineering
Scalable AWS architectures with Bedrock, EKS, Lambda, and CI/CD pipelines for secure operations
Distributed ETL with Apache Spark, Iceberg, and vector databases for intelligent data operations
Enterprise AI agent platform enabling intelligent data analytics automation through specialized agents (SQL, BI, ETL, Analytics) with contextual memory system and comprehensive tool integration for unified data operations.
Multi-modal ML system for real-time housing market crisis prediction and policy recommendation using XGBoost, CatBoost, TimesFM, and STGNN. Deployed on Kubernetes with GPU support for scalable, enterprise-grade forecasting.
Novel approach to improve RAG retrieval accuracy by combining Small Language Models (SLM) and Large Language Models with vector databases for enhanced information retrieval and context understanding.
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