Software Engineer · Backend Systems · Applied AI
Building secure, scalable infrastructure for decision intelligence.
Featured Project
Smart Strategies Builder
AI-powered financial decision intelligence platform
A production-ready financial decision intelligence platform focused on risk analysis, simulation, and explainable insights — not trade execution.
Impact
- Designed and built a full-stack SaaS platform using FastAPI, PostgreSQL, Redis, Docker, and Next.js, with secure authentication, subscription billing, and role-based entitlements.
- Implemented a tiered intelligence access system enforcing feature availability by subscription (risk analytics depth, data latency, simulation limits) with Redis-cached entitlements.
- Architected a simulation-first, explainable approach to financial intelligence, emphasizing risk metrics, regime analysis, and backtesting — explicitly avoiding live trading or profit claims.
Tech Stack
What This Demonstrates
- Production API design (auth, billing, webhooks, entitlements)
- Data-driven feature gating and access control
- Clear separation between intelligence and execution
- Readiness for ML/AI systems (evaluation, explainability, safety)
Key Features
- Secure auth (JWT, MFA, Argon2id)
- Stripe subscriptions + webhooks
- Intelligence entitlements by tier
- Portfolio risk analytics and simulations
- Backtesting and historical replay
- Audit-friendly, explainable outputs
SSB is a financial decision intelligence platform. It does not execute trades, generate buy/sell recommendations, or promise returns.
Other Projects
FinRAG — Finance Domain RAG System
Production-grade Retrieval-Augmented Generation system for financial document intelligence.
A finance-domain RAG system built to analyze SEC filings, earnings reports, and financial documents through grounded LLM responses. Demonstrates full-stack ML engineering ownership across ingestion, chunking, embeddings, vector search, reranking, and API deployment.
Tech Stack
Highlights
- Secure document ingestion pipeline with validation + sanitization
- Finance-aware chunking strategy
- Local embeddings using BGE models
- Custom MMR reranker for result diversity
- Vector DB abstraction layer
- Async LLM querying
- Strict API validation + rate limiting
- Structured logging with Structlog
- Production security architecture
About Me
I'm a software engineer focused on building production systems that are secure, scalable, and built to last.
My work spans full-stack development, backend infrastructure, and applied AI — with a bias toward systems that require real architectural thinking: auth, billing, data pipelines, and decision intelligence platforms. I care about code that ships, systems that don't break at 3am, and abstractions that survive contact with production.
I studied Computer Science, where I built the foundation — Java, Python, C++, algorithms. Since then, I've been sharpening on real projects: designing APIs, implementing secure auth flows, and building SaaS infrastructure from the ground up.
I'm looking for a role where I can own meaningful backend systems, contribute to ML/AI-adjacent products, and work alongside engineers who take craft seriously.
Skills & Technologies
Languages
Backend
Frontend
DevOps
Security
ML / AI
Contact Me
Email: standleydowell@gmail.com