Parag Chhalotre
Open to work1 years experienceData Scientist
Data Scientist specializing in RAG, LLMs, and multi-agent systems.
Standing Rank
Rank Not Available
Developer Badges
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Skills & Technologies
37 skillsWork Experience
Data Scientist
Turing
- Integrated internal labeling tools with multi-agent systems using MCP and A2A orchestration, automating complex annotation workflows with tool-scoped agents.
- Built and optimized production RAG pipelines (document ingestion, vector indexing, retriever–reranker, prompt engineering) to improve grounding, labeling accuracy, and response consistency.
- Establish evaluation and monitoring pipelines for accuracy, faithfulness, latency, hallucination control, and cost, enabling continuous optimization and scalable deployment.
Projects
dAiRaG
- Built an end-to-end graph-grounded SAP Order-to-Cash analytics app with Neo4j, FastAPI, React, and NL-to-Cypher, enabling natural-language exploration of complex business process data.
- Engineered a reliable NL-to-Cypher QA pipeline with query rewriting, guardrails, grounded responses, and caching, reducing repeated inference overhead and improving answer consistency.
- Added Langfuse-based observability and evaluation for tracing, token/cost tracking, and answer-quality scoring, then containerized and deployed the app with Docker, Render, and Neo4j AuraDB.
Plum
- Built an AI-powered claims adjudication system integrating LLM-based document understanding with rule-based policy reasoning to produce explainable and auditable decisions.
- Designed a robust multi-step pipeline with validation, fallback handling, and structured extraction, improving reliability under incomplete or noisy inputs.
- Developed an automated evaluation framework executing 10k+ scenarios with report generation, enabling systematic benchmarking of accuracy and failure cases.
MCS
- Built a Monte Carlo portfolio optimization engine that simulated 10,000 portfolio weight combinations to maximize Sharpe ratio and minimize portfolio risk.
- Engineered a financial modeling workflow to compute returns, covariance, expected return, and volatility from historical market data using Python, Pandas, and NumPy.
- Ran 30-year forward simulations on optimized portfolios and visualized scenario distributions to evaluate long-term growth potential and downside uncertainty.
Leaderboard Standings
Leaderboard Position Pending
Global test scores, peer standing percentiles, and algorithm leaderboard ranks are updated dynamically.
Assessment Highlights
Assessments Not Completed
Coding evaluations, system assessment results, and conceptual score badges will appear here after taking a test.
AI Collaboration Score
AI Collaboration Score Pending
Developer coding behavior, assistant cooperation, and AI pair-programming indicators are evaluated during live coding sessions.
Role Compatibility Profile
Role Compatibility Analysis Pending
Custom matching reports, candidate role compatibility percentiles, and core engineer strength profiles are processed once conceptual code screenings are complete.
Achievements
Resume Match Rate Improvement
Improved resume match rate by 35% using an NLP-based recommender.
Screening Success Rate Increase
Increased screening success rate by 22% through A/B testing.
Engagement Boost
Boosted engagement with recommender system by 30% using SQL-based funnel tracking.
About Details
Professional Bio
Data Scientist with expertise in RAG pipelines, multi-agent systems, and NLP. Experienced in building production-grade AI applications, optimizing LLM workflows, and conducting data-driven analysis. Passionate about scalable deployment and continuous evaluation.
Bachelor of Technology in Chemical Engineering
Indian Institute of Technology Roorkee (2021 - 2025)