Gopal Jaiswal

Engineering across Investment Banking and Equity Derivatives — agentic AI research tooling and ML-driven options analytics.

• Banker Copilot: built a full-stack agentic AI platform (React, TypeScript, FastAPI, LangGraph, LangChain, AWS Bedrock) for multi-agent financial research, secure semantic retrieval over PostgreSQL/pgvector, peer analysis and cited report generation.
• Moved document ingestion and long-running research execution onto Celery + Redis, so agent runs never block the request path.
• Equity Derivatives: built ML-driven options trading analytics in C++, Golang and Python — real-time market data over Kafka, volatility forecasting with XGBoost, Greeks-based risk calculations and historical strategy simulation.
• Shipped the analytics services as containers on Docker and Kubernetes.

Healthcare workflow engine — a multi-tenant EMR platform for clinical operations.

• Built a multi-tenant EMR platform (Java, Spring Boot, PostgreSQL) automating appointment scheduling, clinical workflows and patient record management.
• Event-driven processing over RabbitMQ with configurable state transitions, idempotent execution and retry ladders.
• Audit logging across clinical actions, Redis caching and Dockerised deployment.

Assessment, semantic search and recommendation systems for a high-scale ed-tech platform.

• Assessment Engine: scalable online/offline platform in C++, Rust and Node.js with Kafka, Redis, MongoDB and AWS SQS — 100K+ concurrent users and 80K+ exam responses; cut latency 40% and raised throughput 35%.
• AI Knowledge Engine: semantic question-processing across 2M+ questions (Python, FastAPI, LangChain, Sentence Transformers, FAISS/Pinecone) with OCR/LaTeX extraction and NLP classification — reduced duplicates by 95%.
• Personalized Learning Engine: ML recommendation system (XGBoost, scikit-learn, spaCy) combining performance analytics with semantic question metadata to surface learning gaps — improved engagement by 20%.