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Sidhant Manale

22ML Engineer • AI Developer

I build AI systems end to end. My work spans retrieval pipelines, prompt optimization, multi-agent systems, model training, and performance engineering for document-heavy workflows. I focus on practical ML systems that are reliable, measurable, and useful in real-world settings.

Professional Experience

Binoloop

Binoloop

Machine Learning Engineer

Apr 2025 – Dec 2025

  • Shipped a production RAG system with BM25, dense retrieval, query expansion, reranking, and conversational memory, improving retrieval relevance by 40% and user engagement by 25%.
  • Created an LLM prompt-optimization pipeline with chain-of-thought and few-shot examples, improving regulation and urban-planning document analysis accuracy by 35%.
  • Cut response latency by 78% and memory footprint by 71% via connection pooling, leaner data structures & pipeline refactoring.
  • Automated evaluation with an LLM-as-a-Judge framework using GPT-4.1, reducing manual annotation effort by 80%.
Lamarr

Lamarr

Artificial Intelligence Intern

Sep 2024 – Mar 2025

  • Built FastAPI services to automate scraping of 10,000+ Indian court case files, improving legal data accessibility by 60%.
  • Designed a LangGraph multi-agent system for contract analysis, identifying adversarial clauses with 85% precision.
  • Delivered a production RAG pipeline with hybrid search for a legal Q&A chatbot, improving answer relevance by 40%.
  • Implemented document preprocessing and chunking workflows for long-form legal text, improving retrieval consistency and reducing noisy context in downstream contract and case-law analysis.
Findability Sciences

Findability Sciences

Data Science Intern

Jan 2024 – Jun 2024

  • Built an end-to-end time-series forecasting pipeline with XGBoost and feature engineering, achieving 98.8% accuracy on a 10,000+ row dataset.
  • Developed hierarchical classification datasets across five business domains using custom ontologies.
  • Created model evaluation dashboards and error-analysis reports to compare forecast performance across segments.

GitHub Activity @sidmanale643

Stack I use

Technologies I work with to build AI systems and applications

Python
Python
SQL
SQL
FastAPI
FastAPI
Docker
Docker
PostgreSQL
PostgreSQL
Redis
Redis
TypeScript
TypeScript
React
React
Next.js
Next.js
Node.js
Node.js
Tailwind CSS
Tailwind CSS
Git
Git
MongoDB
MongoDB
Google Cloud
Google Cloud
Azure
Azure
AWS
AWS
Python
Python
SQL
SQL
FastAPI
FastAPI
Docker
Docker
PostgreSQL
PostgreSQL
Redis
Redis
TypeScript
TypeScript
React
React
Next.js
Next.js
Node.js
Node.js
Tailwind CSS
Tailwind CSS
Git
Git
MongoDB
MongoDB
Google Cloud
Google Cloud
Azure
Azure
AWS
AWS
Python
Python
SQL
SQL
FastAPI
FastAPI
Docker
Docker
PostgreSQL
PostgreSQL
Redis
Redis
TypeScript
TypeScript
React
React
Next.js
Next.js
Node.js
Node.js
Tailwind CSS
Tailwind CSS
Git
Git
MongoDB
MongoDB
Google Cloud
Google Cloud
Azure
Azure
AWS
AWS
Python
Python
SQL
SQL
FastAPI
FastAPI
Docker
Docker
PostgreSQL
PostgreSQL
Redis
Redis
TypeScript
TypeScript
React
React
Next.js
Next.js
Node.js
Node.js
Tailwind CSS
Tailwind CSS
Git
Git
MongoDB
MongoDB
Google Cloud
Google Cloud
Azure
Azure
AWS
AWS

If you've read this far, you might be interested in what I do.

Let's connect

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© 2026 Sidhant Manale.