AI Engineer | FinTech & Decision Systems Specialist
Building explainable risk engines, autonomous agents, and production-grade financial AI.
I am a Computer Science Student specializing in Decision Intelligence. I bridge the gap between complex AI research and high-stakes production environments.
My expertise lies in a "Perfect Blend" of two worlds: architecting Autonomous Agent Swarms for complex workflows and engineering Explainable FinTech Engines that prioritize regulatory compliance (RegTech) and capital protection. I build the architecture that allows models to solve real-world economic problems.
Built a production-grade classifier using the Kaggle Credit dataset. Used a "Parsimonious 5" model with SHAP explainability, deployed via Dockerized FastAPI for sub-100ms inference and Render Cloud.
Architected a multi-agent swarm using CrewAI. Orchestrated 4 agents for autonomous market research, demonstrating high-level agentic orchestration and task automation without human intervention.
Engineered a RAG system from scratch using NumPy for vector search logic. Mastered semantic retrieval fundamentals to transform technical documentation into actionable, context-aware AI insights.
Developed a Walk-Forward Validation framework for NIFTY assets. Integrated PyPortfolioOpt while accounting for 0.1% transaction friction to ensure high backtest integrity and eliminate bias.
A Deep Learning MLOps showcase: trained a CNN to 92% accuracy and deployed via FastAPI. Focused on Pydantic validation and containerized serving for stable, real-time production inference.
Combined K-Means Clustering for Market Regime detection with FinBERT sentiment scoring. Identified "Panic" regimes and sentiment divergence to drive Contextual AI decision-making in finance.
I focus on tools that build production-ready software, not just experiments.
I am currently open to AI/ML Engineering and Fintech Internships.
Check out my full CV below.