Work & Research
My career sits at the intersection of AI/ML and quantitative finance. From founding an autonomous investment-agent startup to publishing security research, each role has deepened my conviction that rigorous engineering can solve real problems at scale.
Resume (PDF)September 2026 – Present
AI and ML Researcher
Remy
Building a software engineering harness that produces brand-aware UX.
- Building a software engineering harness to create brand-aware UX
- Designing internal benchmarks to test the harness and identify AI slop
- Post-training SLMs on proprietary data with RL and SFT
June 2025 – August 2026
AI and ML Intern
Keyfactor
Advancing machine learning for public-key infrastructure and certificate lifecycle management.
- Achieved state-of-the-art accuracy predicting X.509 certificate risk using custom ML models trained on large-scale telemetry data
- Lead author on arXiv paper "X-amine509: Predicting the Practical Risk Level of Enterprise X.509 Certificates" (arxiv.org/abs/2609.09402)
- Built secure agentic systems and MCP servers to extend PKI product capabilities with AI-driven automation
December 2025 – September 2026
AI Engineer
Stealth Startup
Building production computer vision models for proprietary perception workloads.
- Trained VLM and RNN-based computer vision models from scratch for proprietary, domain-specific perception tasks
- Drove models to state-of-the-art internal benchmark performance while reducing inference latency and GPU cost under production constraints
January 2025 – Present
AI Researcher
SEE Lab, Dartmouth
Research under Professor SouYoung Jin on multimodal models for human-aligned video understanding.
- Built Meaningful Moments, a corpus of 4.58M VLM temporal importance scores across 500K+ videos, as a senior thesis
- Designed an alpha-parameterized evaluation framework with an importance-inverted control, showing importance-guided sampling preserves accuracy with ~25x fewer model calls
November 2024 – Present
Founder
Brama AI
Building AI agents that expand research coverage for fundamental equity teams.
- Designed and built a team of buy-side AI agents that handle earnings processing, thesis generation, and position monitoring so analysts can cover names they don't have bandwidth for
- Architected RAG pipeline with MongoDB vector search for real-time ingestion of SEC filings and market data
- Deployed full-stack React application on AWS with CI/CD
June 2024 – August 2024
Quant Research / AI Intern
Trivariate Research
Built data infrastructure for quantitative and fundamental equity research.
- Built a fully-automated Python data pipeline for quant and fundamental research workflows
- Processed and cleaned large financial datasets to support systematic investment strategies