Open to opportunities

Rishi Gupta

@rishigupta2

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Final-year AI/ML engineer building LLM-driven RAG systems, production ETL and MLOps pipelines.

India

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What I'm looking for

I seek roles where I can build production-grade ML/NLP systems, improve MLOps pipelines, and deliver measurable model impact in collaborative teams.

I am a final-year B.Tech student specializing in Artificial Intelligence & Machine Learning with hands-on experience building LLM-based RAG systems, production-style ETL pipelines, and end-to-end ML deployments. I have delivered secure cryptographic applications, quantitative trading infrastructure, and real-time prediction services.

My projects include an ECC-based secure payment platform with a Streamlit frontend and modular backend, QuantOracle — a deployed quantitative research and portfolio management platform with production EOD ML pipelines and risk analytics — and an F1 Grand Prix predictor serving low-latency predictions on AWS EKS.

I work with Python, Hugging Face, LangChain/LangGraph, Neo4j, Docker, GitHub Actions, Kubernetes, and AWS, and I focus on production-readiness, reproducible pipelines, and measurable performance gains in real settings.

Experience

Work history, roles, and key accomplishments

Summer Intern

Indian Institute of Technology Guwahati

Jul 2024 - Jul 2024 (0 months)

Built a secure payment platform leveraging Elliptic Curve Cryptography and ECDH for authentication/key exchange, delivered faster and more secure transactions with PCI DSS–oriented compliance features, and released the project open-source for public review.

Python ECC ECDH Streamlit Shadcn Ui Digital Signatures Fraud Detection

AI/ML Project Lead

QuantOracle (personal project)

Architected and deployed an end-to-end quantitative research and portfolio management platform for Indian equities, built production-grade EOD ML pipelines with GitHub Actions and delivered interactive Streamlit dashboards that reduced manual analysis time by ~40%.

Python GitHub Actions Streamlit OHLCV Risk & Analytics

ML Engineer

F1 Grand Prix Predictor (project)

Led feature engineering and model training on telemetry from ~150 sensors, improved podium prediction accuracy from 62% to 78% using ensembles and SHAP, and served low-latency predictions via FastAPI on AWS EKS (~500 req/s, p95 <120ms).

XGBoost LightGBM SHAP fastAPI

Education

Degrees, certifications, and relevant coursework

Vivekananda Institute Of Professional Studies

Bachelor of Technology, Artificial Intelligence & Machine Learning

Grade: 8.117/10

Pursuing a Bachelor of Technology in Artificial Intelligence & Machine Learning with coursework and projects focused on LLM-based RAG systems, ML pipelines, and production deployments.