Software Engineer · Generative AI · Python

I build intelligent systems. And the software that makes them work.

I’m Shivam Bajpai, a Software Engineer working at the intersection of Python automation, Generative AI, LLM evaluation, and Machine Learning. I enjoy turning complex technical ideas into reliable, useful systems.

2.5+ years of professional experienceHCLTech · Microsoft client engagementM.Tech AI & ML · BITS Pilani
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AI core Python LLMs RAG ML

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About

Curiosity starts the idea. Engineering makes it useful.

I work across software engineering and applied AI, with experience in Python automation, enterprise validation, and LLM-enabled workflows. My interests include building retrieval-based AI systems, evaluating model behavior, and developing software that is modular, maintainable, and reliable.

My engineering approach
1 · Understand the problem.Start with the user need, data, and constraints rather than choosing a technology first.
2 · Design the system.Think about architecture, retrieval, validation, error handling, and maintainability.
3 · Make it reliable.Test behavior, investigate failures, and improve the system through evidence.
Professional journey

Where I’ve built things.

Flagship project · In Progress

Making complex public information easier to navigate.

An AI-powered assistant designed to help citizens discover government schemes, understand eligibility, and follow policy updates through a retrieval-based, conversational experience.

PythonLLMsAgentic RAGFastAPIChromaDBPostgreSQL

Smart Citizen Assistant Platform is under development. The architecture below illustrates the intended design; it does not imply every component is built or deployed.


MOCK DATA · NOT OFFICIAL GUIDANCE

Question: “What government schemes might help a family looking for housing assistance?”

Project gallery

Selected projects.

Animations in the gallery are illustrative. No benchmarks or repository links are claimed; add real links in the PROJECTS data below.

RAG laboratory

Inside the intelligence: Explore a RAG pipeline.

Retrieval finds the passages most relevant to a question. Generation writes an answer using only that retrieved context. Good RAG depends on both.

SIMULATION · no live LLM · no real embeddings · scores = word-overlap ÷ query words
Skills constellation

The technologies behind the work.

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GitHub

Explore the code behind the ideas.

Education & certifications
M.Tech, AI & MLBITS Pilani WILP
2026 – 2028
B.Tech, Computer Science EngineeringPranveer Singh Institute of Technology
2019 – 2023
CertificationsAWS · Intro to Machine LearningUdemy · Python for MLAccenture · Digital AIHCLTech · C# ProgrammingGoldman Sachs · Software EngineeringGoogle · Web Designer Basics
Contact

Good engineering starts with an interesting problem.

Looking to connect about software engineering, Python development, Generative AI, LLM evaluation, or applied machine learning? I’d be happy to discuss relevant opportunities and technical challenges.

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