Gawaine O'Gilvie
Senior Software Engineer
Building AI-powered products and scalable backend systems.
- Built internal platforms used by engineering teams at scale
- Developed AI-powered applications (LLMs, RAG pipelines, vision-language models)
Currently focused on the intersection of AI systems and backend infrastructure — building production-grade tools that put language models to work at scale. Most energized by green-field projects, platform problems, and work that requires both systems thinking and AI literacy. Open to full-time roles and contract engagements where technical depth matters.
AI & ML Projects
Signal to Alpha — Options Trading Platform
Retail traders lack systematic tools to capture unstructured Discord trade callouts, execute them across multiple brokers, and analyze performance with AI-powered critique
Built a full-stack options trading platform: 4-stage NLP parsing pipeline (classify → extract → normalize → score), multi-broker execution layer (Schwab OAuth 2.0 + Webull HMAC-SHA1) with Fernet-encrypted token storage, 4 automated trading strategies with shared risk guards (GFV protection, ATR gate, HTF regime filter), and AI trade critique via Claude with tiered market data and per-user rate limiting
End-to-end trade lifecycle from Discord callout to P&L analytics, tax estimation, and journal — 189 pytest tests, live WebSocket price streaming to all connected clients, real-time bot config reloads without restarts
DocSage — RAG Document Intelligence Platform
Teams waste hours manually reviewing large document sets with no semantic search, structured traceability, or grounded AI answers tied to real source passages
Built a production RAG pipeline: multi-format ingestion (PDF with dual-parser fallback, HTML, Markdown), 512-char sliding-window chunking, OpenAI text-embedding-3-small embeddings stored in pgvector with an HNSW index, cosine similarity retrieval via a custom SQL stored function, and GPT-4o synthesis with Pydantic-validated structured output and retry logic via tenacity — deployed on AWS RDS PostgreSQL + Render + Vercel
Live at docsage.phoenix7.dev — every response returns exact citations, token counts, and USD cost; 37-test async pytest suite covering the full RAG stack from ingestion to LLM retry paths
Debugging production incidents required hours of manual log parsing to identify root causes
Built a full-stack app (Next.js + FastAPI) that analyzes production logs using OpenAI GPT-4 to identify errors, cluster patterns, and surface root causes with actionable remediation steps
85% reduction in debugging time for production incidents and CI/CD failures
Core Engineering
Developer Platform Tooling
Engineers lacked standardized workflows for testing and deployment across distributed services
Built internal tooling to streamline CI/CD, contract validation, and deployment workflows for cloud services
Reduced deployment time by 40% and eliminated 85% of contract violations across 10+ microservices
Enterprise Inventory Management Platform
Client needed a scalable system for managing inventory with QR scanning, media uploads, and multi-role access control
Delivered 25+ user stories including authentication, QR code scanning, admin console, and media upload functionality on a modern React/TypeScript stack
Full platform delivered on time with role-based access, real-time updates, and print-optimized QR labels
Experience
Freelance Consulting
Senior Full-Stack Engineer & Technical Consultant
- Built a full-stack options trading platform (Signal to Alpha) with live broker execution, automated strategy bots, and AI trade critique — Next.js 15 / FastAPI / PostgreSQL / Supabase Auth — end-to-end trade lifecycle from Discord callout ingestion to P&L analytics and tax estimation
- Engineered a 4-stage NLP alert parsing pipeline that classifies, extracts, normalizes, and scores unstructured Discord trade callouts into structured OCC option symbols — confidence scoring routes low-quality alerts to a human review queue rather than auto-execution
- Integrated two institutional broker APIs (Schwab OAuth 2.0 and Webull HMAC-SHA1) with Fernet-encrypted token storage, paper trading mode, and a unified order abstraction layer — zero plaintext secrets at rest
- Designed a live-reloadable bot configuration system using a PostgreSQL singleton read on every scheduler tick — risk parameters (stop loss, max positions, GFV guards) take effect in real time during live market sessions without restarts
- Built a real-time WebSocket price streaming layer maintaining persistent connections to Schwab and Webull broker streamers, fan-outing normalized price events to all connected frontend clients — eliminates polling for live P&L and position updates
- Delivered AI-powered trade critique using the Claude API with structured prompts combining parsed callout + live option chain data; per-user rate limiting (slowapi) and TTL caching reduce API cost; yfinance fallback serves free-tier users
Sonos, Inc.
Senior Software Engineer (Cloud Team)
- Migrated 10+ applications to AWS EKS, reducing infrastructure costs by 40% through optimized containerization and resource allocation
- Built Lambda/S3 data pipelines processing millions of events with robust error handling and batch processing for reliable data ingestion
- Developed automation systems that reduced Kubernetes migration time by 80% and streamlined daily deployments for cloud services
- Built developer productivity platforms using React and TypeScript, increasing engineering team satisfaction by 30%
- Mentored junior engineers on TypeScript best practices, AWS architecture patterns, and Kubernetes deployment strategies
Nuance Communications
Software Engineer
- Engineered real-time WebSocket services in Python (Flask, Bottle) handling high-throughput voice recognition traffic in production
- Built and maintained microservices and RESTful APIs supporting speech and analytics workflows across internal and external systems
- Developed monitoring dashboards that improved incident response time by 40% with real-time visibility into system health
- Collaborated with data scientists to optimize speech recognition models and improve accuracy metrics
Technical Skills
Languages
Backend & Systems
AI / ML
Cloud & Infrastructure
Frontend
Databases & Observability
Education
Master of Science, Data Science
University of Texas at Austin
Austin, TX
- Advanced coursework in Machine Learning, Statistical Analysis, and Data Engineering
- Specialization in predictive modeling and big data analytics
Bachelor of Science, Computer Engineering
Northeastern University
Boston, MA
- Focus on Software Engineering and Systems Architecture
- Coursework: Data Structures, Algorithms, Embedded Systems, Computer Networks
Work With Me
I help teams build and ship faster. Available for contract and consulting engagements.
- Build AI-powered applications (LLMs, RAG pipelines, intelligent automation)
- Design scalable backend systems and cloud infrastructure
- Improve developer velocity, CI/CD, and platform tooling
Available for full-time roles and contract / consulting work · Remote or hybrid