Pradip Nichite

Expert-Vetted at Upwork

Mumbai, Maharashtra, India
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Summary

👤
Senior
🎓
Top School
Pradip Nichite is a founder and AI engineering leader with 8+ years of experience building production-grade NLP and ML systems, now scaling custom Generative AI and RAG solutions through his company FutureSmart AI. After pivoting from enterprise roles at MiQ and Oracle, he turned a high-performing freelance practice into a six-figure Upwork business and earned Expert-Vetted status in the top 1% of freelancers. His hands-on toolkit spans LLMs, LangChain/LangGraph, FastAPI, vector stores (Pinecone, Qdrant), and cloud deployments on AWS/GCP, with a track record of shipping fine-tuned Transformer models and deploying them on Fargate. Pradip pairs academic rigor from an M.Tech in Data Science (IIIT Bangalore) with pragmatic product focus—building AI demos and customer-facing apps that turn research into measurable ROI. An under-the-radar strength is his end-to-end fluency from data pipelines and feature engineering to model deployment and interactive demos, making him adept at moving proofs-of-concept into live systems.
code8 years of coding experience
job7 years of employment as a software developer
bookMaster of Technology - MTech Data Science, Master of Technology - MTech Data Science at International Institute of Information Technology Bangalore
bookBachelor of Technology (B.Tech.) Information Technology, Bachelor of Technology (B.Tech.) Information Technology at Dr.Babasaheb Ambedkar Technological University, Lonere - Raigad
bookScience, Science at G.V.Khade Vidyalaya Shahapur,Thane,Maharashtra
languagesEnglish
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Stackoverflow

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Github Skills (10)

transformers10
machine-learning10
streamlit10
aws10
python10
dockers9
fastapi9
deploying9
nlp9
docker9

Programming languages (2)

Jupyter NotebookPython

Github contributions (5)

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Role in this project:
userFull-stack Developer
Contributions:34 commits, 69 pushes, 1 branch in 6 months
Contributions summary:Pradip created and deployed a fine-tuned BERT model for sentiment analysis, suggesting a focus on machine learning and model deployment. They implemented a Streamlit application for real-time sentiment analysis, demonstrating front-end development skills. Furthermore, the user deployed a Transformers model on AWS Fargate, showing proficiency in cloud deployment and containerization technologies. They also built an application with AirTable for data storage.
Contributions:12 pushes in 13 days
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