AnanthaShayan
Machine Learning Engineer
Building intelligent systems that work in the real world — from NLP pipelines to probabilistic forecasting to production-ready AI backends.
I'm a machine learning engineer focused on building systems that solve real problems — not just ones that look good on paper.
My work spans NLP, time-series forecasting, and end-to-end AI pipelines. I care about the full stack: from model design to deployment to making things actually scale.
Currently studying AI at IIT Ropar (Minor) and CSE at JSS Academy, while building things that push beyond coursework.
ML Pipelines
End-to-end systems from data ingestion to model serving
Real-World Systems
Production deployments on GCP, with CI/CD and monitoring
Problem Solving
First-principles thinking applied to messy, real-world data
Languages
Machine Learning
Techniques
Libraries
Backend & Deployment
Tools
Databases
01
AI Job Recommendation System
Endee Vector DB
Semantic job matching system that parses resumes, encodes them as vectors, and ranks job listings by cosine similarity. Surfaces skill gaps alongside match scores.
- Resume parsing + embedding pipeline
- Vector search via Endee Vector DB
- Cosine similarity ranking + skill gap analysis
- Flask REST backend
02
Agricultural Market Price Forecasting
Time-Series ML
Probabilistic multi-series forecasting of mandi prices using DeepAR. Designed to handle the noise and data limitations inherent in agricultural datasets.
- Probabilistic forecasting with DeepAR
- Multi-series Mandi price dataset
- Handles sparse data and uncertainty
- Confidence intervals on predictions
03
SasyaSampada
AI Assistance for Farming
RAG-based agricultural assistant that retrieves domain knowledge to answer farmer queries and recommend crops — built for low-resource, high-impact use.
- RAG pipeline with LangChain
- Agricultural knowledge retrieval
- Crop recommendation engine
- Optimized for low-resource deployment
Founding Tech Lead · Healthrytix HealthTech Solutions
2024 – Present · AI / Automation
- Built end-to-end AI automation workflows using n8n, eliminating manual operational bottlenecks
- Reduced operational costs by 70–80% through intelligent process automation
- Designed and deployed scalable systems on GCP — sole technical contributor across the stack
- Owned architecture decisions from infrastructure to model integration
IIT Ropar
Minor in Artificial Intelligence
2025 – 2026
AI SpecializationJSS Academy of Technical Education
B.Tech — Computer Science & Engineering (AI)
2024 – 2028
CSE · AI/ML MajorHave a project in mind or want to collaborate? Reach out — I'm always open to interesting conversations.