What I Build
Python and FastAPI backends, data pipelines and API integrations, with AI agent and RAG work as a secondary area.
Data Pipelines & Backend
Reliable backend systems and data infrastructure using Python, FastAPI, PostgreSQL, and Redis.
FastAPI Services
REST APIs with async handling, SQLAlchemy ORM, Pydantic validation, and structured error logging.
PostgreSQL & TimescaleDB
Schema design, time-series storage, query optimization, and migration management for data-heavy applications.
Redis Caching & Queuing
Session management, response caching, and lightweight job queues using Redis.
Analytics & Monitoring
Custom metrics pipelines with Prometheus, log aggregation, and dashboard integration for operational visibility.
Real-Time & Data-Intensive Systems
Real-time Python systems, streaming pipelines, and long-running data workloads. Trading systems are one proof domain, not the primary service category.
Real-Time Decision Engines
Long-running Python services that combine streaming inputs, stateful logic, signal generation, and explicit operational controls.
Evaluation & Simulation Harnesses
Reproducible evaluation and simulation layers for systems where model, rule, or strategy behavior must be measured before deployment.
Streaming Data Pipelines
WebSocket and event-driven ingestion, normalization, backpressure-aware processing, and persistent storage for real-time workloads.
State & Regime Modeling
Stateful classification and regime-aware logic for systems that need to adapt behavior as operating conditions change.
Full-Stack Products
Complete web applications from API to UI — React/Next.js frontends, Docker deployments, and CI/CD pipelines.
React / Next.js Frontends
Server-side rendered or statically generated frontends with Tailwind CSS, Framer Motion, and accessible component design.
Data Dashboards
Real-time trading dashboards and analytics views with TradingView Charts and live WebSocket data feeds.
Docker & Deployment
Containerized multi-service deployments with Docker Compose, managed deployments on Railway, GCP, or DigitalOcean.
MVP delivery
End-to-end delivery from concept to deployed product — GitHub Actions CI/CD, environment management, and post-launch support.
Also worked with: AI & LLM prototypes
LLM integrations, agent pipelines and RAG prototypes. These are secondary to the backend work above and are labelled by maturity on the projects page.
LangGraph Agent Pipelines
Multi-step agents using LangGraph for orchestration: planning, tool use and state management.
RAG Systems
Retrieval-Augmented Generation pipelines with FAISS or pgvector, connecting LLMs to structured and unstructured private data.
LLM Integration & API Wrapping
Integrating OSS models (Ollama, vLLM) or hosted APIs (OpenAI, Anthropic) into backend services with streaming and fallback handling.
Evaluation & Observability
Automated LLM eval pipelines, prompt regression testing, and structured logging for inference.