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Schedule Interview NowMy name is Anup K. and I have over 10 years of experience in the tech industry. I specialize in the following technologies: Python, R, Data Lake, AWS Lambda, Data Analysis, etc.. I hold a degree in Bachelor of Engineering (BEng), Master of Technology (MTech), Doctor of Philosophy (PhD). Some of the notable projects I’ve worked on include: PDF Q&A Bot with LangChain, Agentic Credit Risk Management, LLM agent for database, Agentic coding assistance, RAG for PDFs, etc.. I am based in Nagpur, India. I've successfully completed 6 projects while developing at Softaims.
I value a collaborative environment where shared knowledge leads to superior outcomes. I actively mentor junior team members, conduct thorough quality reviews, and champion engineering best practices across the team. I believe that the quality of the final product is a direct reflection of the team's cohesion and skill.
My experience at Softaims has refined my ability to effectively communicate complex technical concepts to non-technical stakeholders, ensuring project alignment from the outset. I am a strong believer in transparent processes and iterative delivery.
My main objective is to foster a culture of quality and accountability. I am motivated to contribute my expertise to projects that require not just technical skill, but also strong organizational and leadership abilities to succeed.
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Amazon India
Built an AI-powered chatbot that allows users to ask questions from PDF documents using natural language. Used LangChain with OpenAI embeddings and a vector store (FAISS/Chroma) to chunk, index, and retrieve contextually relevant answers. Integrated streamlit-based frontend for easy file upload and interactive Q&A interface. Enabled multi-document support and citation-based answers, improving document comprehension for legal and academic use cases.
Developed an LLM-based agentic workflow to automate credit risk assessment using applicant data, financial history, and external reports. The agent parsed documents, performed ratio analysis, and generated risk profiles with explainable recommendations. Integrated LangChain agents to orchestrate sub-tasks like data extraction, eligibility checks, and policy rule matching. Deployed in a banking environment to accelerate loan approvals while ensuring regulatory compliance and reducing default risks.
Developed an LLM-powered agent to enable natural language queries on enterprise databases, eliminating the need for SQL expertise. Integrated OpenAI/GPT models with Langchain and custom prompt engineering to generate, validate, and execute SQL queries dynamically. Implemented secure data access layers, query optimization, and response formatting for accurate and explainable outputs. Deployed in a banking environment to assist staff in retrieving insights from customer, transaction, and compliance datasets using plain English.
Built an LLM-driven agentic assistant to help developers write, debug, and refactor code across Python and SQL projects. Used LangChain agents to break down user intent, access documentation, suggest code snippets, and run test cases in sequence. Integrated tools like code interpreter, static analysis, and vector-based memory for context retention and iterative improvement. Enabled faster onboarding for junior developers and improved code quality through intelligent, context-aware recommendations.
Implemented a RAG pipeline to answer user queries from PDF documents with high accuracy and contextual relevance. Used LangChain to extract, chunk, and embed PDF content into a vector store (FAISS/Chroma), enabling semantic search. Combined vector-based retrieval with LLM-generated responses to ensure answers are grounded in source documents. Ideal for legal, academic, and policy documents, the system supports citations and multi-page contextual reasoning.
Bachelor of Engineering (BEng) in Computer science & Enginnering
1999-01-01-2003-12-01
Master of Technology (MTech) in Computer science & Engineering
2014-01-01-2016-01-01
Doctor of Philosophy (PhD) in Artificial Intelligence