# Onkar Jadhav

**AI / Full Stack Software Engineer**  
Pune, Maharashtra  
Bachelor of Technology in Computer Science  

📞 +91-7058604679  
📧 onkarj012@gmail.com  
🔗 GitHub: github.com/Onkarj012  
🔗 LinkedIn: linkedin.com/in/onkarj012  

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# Summary

Final-year Computer Science student at PCCOE (9.08 CGPA) with hands-on experience building end-to-end AI systems, including an open-source LLM governance benchmark evaluated across the US, India, and EU legal scenarios, and a deep learning equity prediction system covering Nifty100 NSE equities. Proficient in Python, React.js, Node.js, FastAPI, and LangGraph.

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# Education

## Bachelor of Technology in Computer Science
**Pimpri Chinchwad College of Engineering, Savitribai Phule Pune University**  
**Nov 2022 -- Jun 2026**  
CGPA: **9.08** (up to 7th semester)

### Relevant Coursework
- Machine Learning
- Data Structures & Algorithms
- Database Management Systems
- Operating Systems
- Computer Networks
- Object-Oriented Programming

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## Higher Secondary Certificate
**Shri Mhalsakant Junior College, Maharashtra State Board**  
**Jun 2022**  
Score: **89.17%**

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# Engineering Projects

## StockXpert — AI-Powered Equity Prediction & Stock Recommendation Platform
**Tech Stack:** Python, TensorFlow, Next.js, FastAPI, TypeScript, Tailwind CSS, Cloudflare R2, AWS  
**Duration:** Nov 2025 -- Present

- Architected a hierarchical deep learning system combining ResNet, GRU, Bi-LSTM, and multi-head attention to capture cross-horizon price dependencies across **Nifty100 NSE equities**, outperforming single-architecture baselines.
- Constructed a leakage-free feature pipeline from raw OHLCV data incorporating RSI, MACD, ADX, Bollinger Bands, SMA/EMA distance, and volatility indicators across strict train/validation/test splits.
- Implemented a multi-objective Huber loss with directional regularization and tail-event oversampling, achieving a **12% improvement in short-horizon return correlation** over baseline and reducing directional error on high-volatility equities.
- Designed a snapshot-first serving layer with Cloudflare R2 storage and market-aware TTL caching, delivering sub-second API responses across **all 100 Nifty stocks** during market hours while eliminating redundant inference costs.
- Built a full-stack trading dashboard (Next.js + FastAPI) featuring a live stock screener with real-time alerts, OHLCV candlestick charts, technical indicator overlays, and forecast tables (1D–10D) with calibrated confidence scores.

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## GovBench — Open-Source LLM Governance Benchmark
**Tech Stack:** Python, LLM Evaluation, AI Safety, Sentiment Analysis, Hallucination Detection  
**Duration:** Mar 2026 -- Present

- Designed and open-sourced GovBench on GitHub, an LLM evaluation framework assessing model readiness for judicial and governmental deployment across **6 pillars**: demographic bias, procedural integrity, corruption resistance, jurisdictional awareness, transparency, and minority protection.
- Implemented controlled demographic isolation testing across **12 identity variants** per scenario with naturalistic prompting, measuring inherent bias in bail, sentencing, welfare, and immigration decisions across the US, India, and EU legal systems.
- Evaluated 4 production LLMs (Claude Sonnet 4.6, Gemini Flash Lite, DeepSeek V4, GPT-OSS 120B) across baseline, pressure, and adversarial modes, revealing top performers scored **90%+ overall** while identifying failures in minority protection (GPT-OSS: 63%) and jurisdictional awareness (DeepSeek V4: 56.7%).
- Built an automated scoring pipeline combining sentiment variance analysis, hallucination detection, and position drift tracking to produce composite deployment-readiness grades across evaluated models.

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## PromptForge — Agentic Prompt Engineering Platform
**Tech Stack:** Node.js, Express.js, BullMQ, Convex, OpenRouter, React.js  
**Duration:** Jan 2026 -- Feb 2026

- Developed a platform for designing, testing, and optimizing LLM prompts, enabling developers and AI product teams to generate, refine, and structure prompts for faster delivery and more reliable outputs.
- Architected a creator-critic-evaluator pipeline that iteratively improves prompts through automated feedback, scoring clarity, completeness, constraints, and token efficiency across multiple versions.
- Implemented cross-model routing and benchmarking across OpenAI, Anthropic, Gemini, and open models, enabling direct comparison of quality, latency, and cost per prompt for model selection decisions.
- Built a fault-tolerant async backend using BullMQ workers and Convex persistence, supporting idempotent job retries and end-to-end logging for reliable prompt regression testing at scale.

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# Technical Skills

## Languages
- Python
- JavaScript
- SQL
- C++
- Java

## Frontend
- React.js
- HTML
- CSS

## Backend
- Node.js
- Express.js
- FastAPI
- REST APIs
- BullMQ

## AI / ML
- TensorFlow
- XGBoost
- LangGraph
- OpenRouter
- LLM Evaluation
- RAG Pipelines
- Agentic Workflows

## Databases
- MongoDB
- PostgreSQL
- Convex

## Cloud & DevOps
- AWS
- Git
- GitHub