class TanishqJain:
def __init__(self):
self.role = "AI Engineer & Backend Developer"
self.school = "B.Tech CSE (AI & ML) @ JECRC Jaipur — CGPA 8.11"
self.builds = ["ML systems", "REST APIs", "data pipelines", "full-stack apps"]
self.stack = ["Python", "FastAPI", "PyTorch", "Docker", "Next.js", "TypeScript"]
self.approach = "Own it end to end — model, service, container, deploy"
def currently(self):
return "Shipping production AI systems and contributing to OSS"I'm an AI engineer who lives on the backend. Most of my work is the unglamorous half of ML: designing the APIs, pipelines, auth and containers that turn a model into something people can actually call.
Applied ML is where I started — speech scoring, retrieval-augmented generation, local LLM inference — but the through-line is systems that ship, scale and stay up, not notebooks.
| Languages | |
| Backend & APIs | |
| AI & ML | |
| Frontend | |
| Infra & Tools |
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AI-powered speech transcription & scoring End-to-end pipeline that transcribes and scores Spanish Elicited Imitation Test recordings.
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Full-stack AI platform with RAG Open-source, fully offline AI platform with a custom vector store and local LLM inference.
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Contract risk-screening system Flags LOW / MEDIUM / HIGH labour-law compliance risk in employment contracts.
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Real fixes in real codebases
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- 🎯 Qualified for the Amazon ML Summer School 2026 selection test, shortlisted in the top 30,000 of 130,000+ applicants
- ⭐ 5-Star Problem Solving badge on HackerRank
- 🔬 Collaborated with the CERN Human-AI Team on speech transcription and scoring for second-language acquisition research
- 🛡️ Built an explainable anomaly-detection system for Aadhaar demographic analysis at the UIDAI Hackathon
Moving from rule-based to ML-based. From "in progress" to shipped.