Ashutosh Kumar Jaiswal

Backend and AI Systems Engineer who ships full-stack, LLM-integrated applications end-to-end — from RAG pipelines and production APIs at FWC IT Services and Springer Capital, to a sub-millisecond search engine serving 10M+ documents and an autonomous multi-agent research pipeline. 18 months evaluating AI-generated code at Outlier.ai sharpened an eye for correctness, architecture, and engineering rigor across five languages. Backed by a top-10 AI Mathematical Olympiad finish, a 6-Star CodeChef rating, and hackathon recognition from Google DeepMind and Goldman Sachs. Ready to build reliable, scalable systems from day one.

AI Full Stack Software Developer - FWC IT Services Pvt Ltd

(2026-01 - 2026-06)

• Engineered end-to-end AI applications integrating LLMs, RAG pipelines, and prompt engineering into full-stack apps.
• Developed production-ready backend services and REST APIs using FastAPI/Flask with PostgreSQL and MongoDB.
• Built responsive web interfaces integrated with AI/ML services for real-time inference and semantic search.
• Containerized and deployed AI applications using Docker and CI/CD, improving deployment consistency and scalability.

Machine Learning & LLM Intern - Springer Capital

(2025-05 - 2025-11)

• Diagnosed and resolved baseline model underperformance, re-engineering pipelines to improve predictive accuracy.
• Architected and deployed an end-to-end LangChain RAG pipeline for document querying and knowledge extraction.
• Designed rigorous model evaluation workflows, benchmarking iterations using F1-score, precision, and recall.
• Managed the ML lifecycle end-to-end in Python, PyTorch, and Scikit-learn — from data prep to validation.

Full Stack Software Engineer, AI Training - Outlier.ai

(2024-11 - 2026-05)

• Reviewed and debugged AI-generated Python, JavaScript, Java, Go, and C++ code for correctness and maintainability.
• Authored technical assessments on full-stack architecture, evaluated against real-world engineering standards.
• Audited Git-based workflows — branching, merge strategy, code review — to identify gaps against industry standards.
• Shaped model training data through structured, high-signal feedback, improving code quality and engineering rigor.

Bachelor of Technology (B.Tech) - Computer Science & Engineering - Lovely Professional University (2022-08 - 2026-05)

Intermediate - Raj Narayan College (2020)

Matriculation - ST John's Academy (2018)