Apr 2025 – Present · 1 yr 1 mo

• Architected a two-stage RAG pipeline integrating Data Ingestion and Query Processing components for efficient document retrieval and AI-assisted response synthesis.
• Developed the Data Ingestion Block to process and embed textual data (documents, notes, and unstructured text) into high-dimensional vector representations (≥768 dimensions) using large language models (LLMs), storing results in a vector database.
• Built the Query Block, converting user queries into embeddings and performing cosine similarity or approximate KNN searches across the embedding space to identify the top three contextually relevant documents.
• Enhanced the final response generation by integrating retrieved context into an LLM-driven synthesis process, ensuring coherent and contextually accurate outputs.
• Optimized the end-to-end RAG flow, reducing latency and improving retrieval accuracy for internal document search and automation use cases.

Jan 2024 – Apr 2025 · 1 yr 3 mos

• Spearheaded AI/ML integration initiatives, developing Python-based solutions for semantic search, document similarity, and automated web scraping using BeautifulSoup and Requests, enhancing data accessibility and workflow efficiency.
• Led critical components of a large-scale migration project, demonstrating strong system architecture knowledge, analytical thinking, and problem-solving skills to ensure smooth transitions and minimal downtime.
• Experimented with vector embedding models such as MS MARCO and FAISS, significantly improving internal document retrieval accuracy and automation performance.
• Contributed to production support, identifying and resolving system issues, optimizing infrastructure performance, and collaborating with cross-functional teams to ensure reliability.
• Implemented and maintained CI/CD pipelines leveraging Docker and Jenkins, streamlining deployment automation and improving overall delivery efficiency.

Dec 2021 – Jan 2024 · 2 yrs 1 mo

• Collaborated closely with internal teams to analyze user requirements and design scalable, technical solutions aligned with business goals and system capabilities.
• Set up, maintained, and enhanced a robust continuous integration and build infrastructure, ensuring seamless development and deployment workflows.
• Led the deployment of releases into production, overseeing end-to-end delivery, monitoring system stability, and mitigating deployment risks.
• Implemented automation in CI/CD pipelines using Jenkins, Docker, and GitHub, reducing manual overhead and accelerating release cycles.
• Developed and optimized backend components leveraging Python, FastAPI, and PostgreSQL, contributing to data-driven decision-making and operational efficiency.
• Explored and applied NLP techniques and graph-based modeling with Neo4j to enhance data connectivity and knowledge retrieval across internal systems.
• Demonstrated strong communication and logical reasoning skills, fostering collaboration between technical and non-technical stakeholders to ensure successful solution delivery.