Headline

AI & Automation Engineer | BIT Mesra ’25 | MERN • GenAI

About

I’m Shreyansh, a Computer Science graduate from BIT Mesra with hands-on experience in Full-Stack Development, AI/ML, and Workflow Automation. I enjoy building fast, scalable, and user-friendly applications—combining modern web technologies with intelligent automation. I recently interned as an SDE at Tata Steel, where I helped digitize internal processes, improve UI responsiveness, and build secure API services for real-world use cases. I also worked as a Data Science Intern at the Mines & Geology Department, developing ML models for mineral classification with 90%+ accuracy. My projects span across: • AI / GenAI — RAG systems, GPT Agents, image enhancement, automated document assistants • Automation — n8n workflows, multi-agent automation, API pipelines • Full-Stack — MERN apps, real-time collaboration, secure file-sharing systems I’m currently open to AI+Full-Stack, Backend, and Automation Engineering roles where I can build meaningful, production-grade systems and grow with high-impact teams

Location

Jamshedpur

AI stack

antigravity

claude code

claude

codex

gemini

hermes agent

Work experience

Software Engineer at CaratSense AI, Feb 2026 - Present · 9 mos, Mumbai, Maharashtra, India

Software Developer Intern at TATA STEEL TECHNICAL SERVICES LTD, May 2024 - Jul 2024 · 3 mos, Jamshedpur, Jharkhand, India, • Developed a scalable web application to digitize internal workflows, reducing manual processing time by 40%. • Engineered responsive UI components using modern front-end tools, improving load performance by 25%. • Built and integrated secure REST API endpoints ensuring reliable backend communication. • Created real-time analytics dashboards, boosting internal data visibility by 35%. • Collaborated with cross-functional teams using Agile practices to ship improvements faster.

Data Science Intern at Mines & Geology Dept., Jharkhand, Jun 2023 - Jun 2023 · 1 mo, Ranchi, Jharkhand, India, • Processed 5,000+ mineral images using PyTorch, OpenCV & Scikit-learn for feature extraction and augmentation. • Achieved 90.5% accuracy with CNN models and 87.3% using Random Forest across 10 mineral classes. • Reduced preprocessing time by 30% using optimized batching & transformations. • Built a Streamlit-based real-time mineral classification app used by 100+ testers.

Education

Bachelor of Technology - BTech, Computer Science, Birla Institute of Technology, Mesra

Matriculation, Computer Games and Programming Skills, Carmel junior college