Applied AI @ MENA AI
Kanishka Utagikar
Applied AI @ MENA AI · remote
I design and ship end-to-end applied AI systems, including retrieval architectures and high-performance backend services.
Experience
Applied AI Intern — MENA AI (Dubai · remote)
Shipping agentic pipelines end to end: tool-using flows, structured routing, evaluation hooks, and the observability you need when LLM steps are chained in production—not one-off notebook demos.
Education
B.E. Information Technology — Thadomal Shahani Engineering College, Mumbai
Third year · CGPA 8.4/10 · expected 2027
Engineering focus
I build the systems around models: agentic pipelines with LangGraph (routing, reflection, human-in-the-loop), parent-document RAG on Supabase/pgvector, and FastAPI + React stacks with OAuth, streaming, and LangSmith-style tracing—so ideas survive contact with real users and traffic.
Skills
Pulled from my resume—grouped for scanning. Not exhaustive of every side project.
Languages
Python, Java, C++, JavaScript, TypeScript, SQL
Generative AI & NLP
LangGraph, RAG pipelines, LangChain, Hugging Face, agentic architectures, LangSmith, vector DBs
Frameworks & cloud
FastAPI, React, Docker, Flask, Streamlit, Git, AWS, Temporal
ML & data
PyTorch, TensorFlow, Scikit-learn, Pandas
Deployment
Render · Railway · Hugging Face Spaces · Vercel
Databases
Supabase · PostgreSQL · MongoDB · pgvector (vector search on Postgres)
Achievements
Certifications: Machine Learning A-Z (Udemy) · Neural Networks and Deep Learning (Coursera) · Complete Python Bootcamp (Udemy)
Neural inference demo
Pick a digit to animate a toy network (visualization only, not a trained model).
WAITING FOR INPUT…