2+ /
Years of experience
5+ /
National Hackathon Win's
10+ /
AI Projects Deployed
1x/2x/3x
Trading Retruns
2+ /
Years of experience
5+ /
National Hackathon Win's
10+ /
AI Projects Deployed
1x/2x/3x
Trading Retruns
{02} — Tools & Skills
My creative toolbox
{02} — Tools & Skills
My creative toolbox
{02} — Tools & Skills
My creative toolbox
Programming
Python | C++ | SQL/MySQL | Solidity | DSA
90
%
Programming
Python | C++ | SQL/MySQL | Solidity | DSA
90
%
Programming:
Python | C++ | SQL/MySQL | Solidity | Data Structures & Algorithms | OS
90
%
DevOps
GCP | AWS | Kubernetes | Docker | Firebase (Firestore) | BigQuery
90
%
DevOps
GCP | AWS | Kubernetes | Docker | Firebase (Firestore) | BigQuery
90
%
Machine Learning & AI
RAG | MCP | LLM | Computer Vision (OpenCV, OCR, Tesseract, TrOCR) | AI Chat- bots | Seaborn | Langchain | HuggingFace | Rest API | Postman | ADK | Streamlit
90
%
HFT & Systems
HFT | Market Microstructure | Derivatives Pricing | Statistical Arbitrage | Risk Metrics (Sharpe/- Sortino) | Portfolio Risk Management | Multi-asset Trading | Algorithmic Strategy Design
90
%
HFT & Systems
HFT | Market Microstructure | Derivatives Pricing | Statistical Arbitrage | Risk Metrics (Sharpe/- Sortino) | Portfolio Risk Management | Multi-asset Trading | Algorithmic Strategy Design
90
%
DevOps
GCP | AWS | Kubernetes | Docker | Firebase (Firestore) | BigQuery
90
%
Machine Learning & AI
RAG | MCP | LLM | Computer Vision (OpenCV, OCR, Tesseract, TrOCR) | AI Chat- bots | Seaborn | Langchain | HuggingFace | Rest API | Postman | ADK | Streamlit
90
%
Machine Learning & AI
RAG | MCP | LLM | Computer Vision (OpenCV, OCR, Tesseract, TrOCR) | AI Chat- bots | Seaborn | Langchain | HuggingFace | Rest API | Postman | ADK | Streamlit
90
%
Quant & Systems
HFT | Market Microstructure | Derivatives Pricing | Statistical Arbitrage | Risk Metrics (Sharpe/- Sortino) | Portfolio Risk Management | Multi-asset Trading | Algorithmic Strategy Design
90
%
Quant & Systems
HFT | Market Microstructure | Derivatives Pricing | Statistical Arbitrage | Risk Metrics (Sharpe/- Sortino) | Portfolio Risk Management | Multi-asset Trading | Algorithmic Strategy Design
90
%
Quant Trading
BlockChain / WEB3
Software Engineering
Business analyst & VC
AI Developer
AI Developer
Quant Trading
Software Engineering
Business analyst & VC
BlockChain / WEB3
AI Developer
Quant Trading
Software Engineering
Business analyst & VC
BlockChain / WEB3
AI Developer
Quant Trading
Software Engineering
Business analyst & VC
BlockChain / WEB3
{03} — WorkEx
Work Experience
Nov’25 - Dec’26 | New York - USA (Remote)
/
Amazon x Extern
Operational Strategy & Poeple Analytics Data & ML Extern
Cloud-Native NLP Engineering:
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
People Analytics & Strategy Insights:
Identified attrition and productivity drivers to support operational decision-making.
ML-Driven Decision Support:
Converted complex data into clear, decision-ready strategy insights.
Nov’25 - Dec’26 | New York - USA (Remote)
/
Amazon x Extern
Operational Strategy & Poeple Analytics Data & ML Extern
Cloud-Native NLP Engineering:
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
People Analytics & Strategy Insights:
Identified attrition and productivity drivers to support operational decision-making.
ML-Driven Decision Support:
Converted complex data into clear, decision-ready strategy insights.
Nov’25 - Dec’26 | New York - USA (Remote)
/
Amazon x Extern
Operational Strategy & Poeple Analytics Data & ML Extern
Cloud-Native NLP Engineering:
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
People Analytics & Strategy Insights:
Identified attrition and productivity drivers to support operational decision-making.
ML-Driven Decision Support:
Converted complex data into clear, decision-ready strategy insights.
July’25 - Oct’25 | Singapore (Hybrid)
/
Mega Forte
AI Developer Intern
OCR Pipeline Development:
Built an automated OCR system to digitize handwritten answer sheets accurately.
Document Intelligence:
Converted unstructured handwritten data into structured, machine-readable formats.
Evaluation Automation:
Enabled faster and more consistent assessment through AI-driven text extraction.
July’25 - Oct’25 | Singapore (Hybrid)
/
Mega Forte
AI Developer Intern
OCR Pipeline Development:
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
Document Intelligence:
Identified attrition and productivity drivers to support operational decision-making.
Evaluation Automation:
Converted complex data into clear, decision-ready strategy insights.
July’25 - Oct’25 | Singapore (Hybrid)
/
Mega Forte
AI Developer Intern
OCR Pipeline Development:
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
Document Intelligence:
Identified attrition and productivity drivers to support operational decision-making.
Evaluation Automation:
Converted complex data into clear, decision-ready strategy insights.
June’25 - Aug’25 | Bangalore - IND, (Hybrid)
/
PipRaiser
Capital Market Analyst Intern
Systematic Trading:
Generated 165.6% returns using macro overlays and order-flow analysis.
Risk Management:
Enforced a strict 5% max risk framework with VaR-based controls.
Multi-Asset Analysis:
Applied cross-asset signals to navigate diverse market regimes.
June’25 - Aug’25 | Bangalore - IND, (Hybrid)
/
PipRaiser
Capital Market Analyst Intern
Systematic Trading:
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
Risk Management:
Identified attrition and productivity drivers to support operational decision-making.
Multi-Asset Analysis:
Converted complex data into clear, decision-ready strategy insights.
June’25 - Aug’25 | Bangalore - IND, (Hybrid)
/
PipRaiser
Capital Market Analyst Intern
Systematic Trading:
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
Risk Management:
Identified attrition and productivity drivers to support operational decision-making.
Multi-Asset Analysis:
Converted complex data into clear, decision-ready strategy insights.
Apr’25 - May’25 | Anfa - Casablanca (Remote)
/
Lotus Capital
Quant Trader Trainee
Liquidity Execution:
Deployed institutional liquidity across commodities and FX markets.
Order-Flow Analytics:
Built Python frameworks for order-flow tagging and alpha decay analysis.
Exposure Control:
Maintained disciplined <1.2% risk per position with drawdown control.
Apr’25 - May’25 | Anfa - Casablanca (Remote)
/
Lotus Capital
Quant Trader Trainee
Liquidity Execution:
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
Order-Flow Analytics:
Identified attrition and productivity drivers to support operational decision-making.
Exposure Control:
Converted complex data into clear, decision-ready strategy insights.
Apr’25 - May’25 | Anfa - Casablanca (Remote)
/
Lotus Capital
Quant Trader Trainee
Liquidity Execution:
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
Order-Flow Analytics:
Identified attrition and productivity drivers to support operational decision-making.
Exposure Control:
Converted complex data into clear, decision-ready strategy insights.
Aug’24 - Sep’24 | San Fransico - USA (Remote)
/
HP Ventures
Business Analyst & VC Extern
Investment Due Diligence:
Conducted deep diligence on $130M+ Series-A AI investments.
Thesis Modeling:
Built high-conviction investment theses with data-backed valuation models.
Market Analysis:
Analyzed sector trends and sales lift under valuation compression scenarios.
Aug’24 - Sep’24 | San Fransico - USA (Remote)
/
HP Ventures
Business Analyst & VC Extern
Investment Due Diligence:
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
Thesis Modeling:
Identified attrition and productivity drivers to support operational decision-making.
Market Analysis:
Converted complex data into clear, decision-ready strategy insights.
Aug’24 - Sep’24 | San Fransico - USA (Remote)
/
HP Ventures
Business Analyst & VC Extern
Investment Due Diligence:
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
Thesis Modeling:
Identified attrition and productivity drivers to support operational decision-making.
Market Analysis:
Converted complex data into clear, decision-ready strategy insights.
Jul’24 - Sep’24 | New York- USA (Remote)
/
HeadStarter AI
Software Engineering Intern
SaaS Architecture:
Helped architect AI-driven SaaS platforms across Fin, Legal, and Ops domains.
Engineering Leadership:
Led a cross-border team delivering production-ready AI systems.
Product Delivery:
Shipped scalable systems aligned with accelerated engineering timelines.
Jul’24 - Sep’24 | New York- USA (Remote)
/
HeadStarter AI
Software Engineering Intern
SaaS Architecture:
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
Engineering Leadership:
Identified attrition and productivity drivers to support operational decision-making.
Product Delivery:
Converted complex data into clear, decision-ready strategy insights.
Jul’24 - Sep’24 | New York- USA (Remote)
/
HeadStarter AI
Software Engineering Intern
SaaS Architecture:
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
Engineering Leadership:
Identified attrition and productivity drivers to support operational decision-making.
Product Delivery:
Converted complex data into clear, decision-ready strategy insights.
May’24 - July’24 | Raipur- IND (Hybrid)
/
IIIT Naya Raipur
AI Developer Research Intern
Transformer Research: Built a T5-based model for multi-class software smell detection.
Built a T5-based model for multi-class software smell detection.
Semantic Modeling:
Leveraged domain-specific semantic alignment to improve precision.
Model Performance:
Achieved strong F1-scores through optimized architecture and evaluation.
May’24 - July’24 | Raipur- IND (Hybrid)
/
IIIT Naya Raipur
AI Developer Research Intern
Transformer Research: Built a T5-based model for multi-class software smell detection.
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
Semantic Modeling:
Identified attrition and productivity drivers to support operational decision-making.
Model Performance:
Converted complex data into clear, decision-ready strategy insights.
May’24 - July’24 | Raipur- IND (Hybrid)
/
IIIT Naya Raipur
AI Developer Research Intern
Transformer Research: Built a T5-based model for multi-class software smell detection.
Built NLP pipelines on Google Cloud to analyze large-scale unstructured workforce data.
Semantic Modeling:
Identified attrition and productivity drivers to support operational decision-making.
Model Performance:
Converted complex data into clear, decision-ready strategy insights.
₹4.7L+
2000%+ Returns
Net Alpha Generated Across live options
₹4.7L+
2000%+ Returns
Net Alpha Generated Across live options
₹4.7L+
2000%+ Returns
Net Alpha Generated Across live options
4+
High-Impact Roles
Quant, AI, SDE, WEB3
4+
High-Impact Roles
Quant, AI, SDE, WEB3
4+
High-Impact Roles
Quant, AI, SDE, WEB3
10+
Production Systems Built
Trading, AI SaaS, OCR & Analytics pipelines
10+
Production Systems Built
Trading, AI SaaS, OCR & Analytics pipelines
10+
Production Systems Built
Trading, AI SaaS, OCR & Analytics pipelines
{05} — FAQ
Got Questions?
Got Questions?
01/
What makes me different from typical candidates?
I don’t separate theory from execution. Every model, strategy, or system I build is designed with deployment, failure modes, and real capital impact in mind.
02/
What kind of results i have delivered?
I’ve generated verifiable PnL and alpha across multiple environments—ranging from high-frequency options strategies to macro-driven multi-asset systems—while maintaining strict risk controls (Sharpe/Sortino-focused, drawdown-aware).
03/
Am i more of a trader or an engineer?
Both. I design systems first—research pipelines, execution logic, and risk frameworks—then deploy them in live or semi-live environments. The goal isn’t trades; it’s repeatable edge.
04/
What problems do i like solving?
Hard ones—where incentives, data, and uncertainty collide. This includes market microstructure, volatility dynamics, AI model reliability, and scaling decision systems under noisy conditions.
05/
What am i actually work on?
I work at the intersection of quantitative trading, applied AI, and systems engineering—building strategies, models, and products that operate under real-world constraints like risk, latency, and scale.
01/
What makes me different from typical candidates?
I don’t separate theory from execution. Every model, strategy, or system I build is designed with deployment, failure modes, and real capital impact in mind.
02/
What kind of results i have delivered?
I’ve generated verifiable PnL and alpha across multiple environments—ranging from high-frequency options strategies to macro-driven multi-asset systems—while maintaining strict risk controls (Sharpe/Sortino-focused, drawdown-aware).
03/
Am i more of a trader or an engineer?
Both. I design systems first—research pipelines, execution logic, and risk frameworks—then deploy them in live or semi-live environments. The goal isn’t trades; it’s repeatable edge.
04/
What problems do i like solving?
Hard ones—where incentives, data, and uncertainty collide. This includes market microstructure, volatility dynamics, AI model reliability, and scaling decision systems under noisy conditions.
05/
What am i actually work on?
I work at the intersection of quantitative trading, applied AI, and systems engineering—building strategies, models, and products that operate under real-world constraints like risk, latency, and scale.
01/
What makes me different from typical candidates?
I don’t separate theory from execution. Every model, strategy, or system I build is designed with deployment, failure modes, and real capital impact in mind.
02/
What kind of results i have delivered?
I’ve generated verifiable PnL and alpha across multiple environments—ranging from high-frequency options strategies to macro-driven multi-asset systems—while maintaining strict risk controls (Sharpe/Sortino-focused, drawdown-aware).
03/
Am i more of a trader or an engineer?
Both. I design systems first—research pipelines, execution logic, and risk frameworks—then deploy them in live or semi-live environments. The goal isn’t trades; it’s repeatable edge.
04/
What problems do i like solving?
Hard ones—where incentives, data, and uncertainty collide. This includes market microstructure, volatility dynamics, AI model reliability, and scaling decision systems under noisy conditions.
05/
What am i actually work on?
I work at the intersection of quantitative trading, applied AI, and systems engineering—building strategies, models, and products that operate under real-world constraints like risk, latency, and scale.
{06} — Connect with me
