Python Backend Engineer | Focus on Async Systems & Scalable Architecture

Backend Development

• Core: Python (Asyncio), Pydantic v2, Static Typing (Mypy).
• Frameworks: FastAPI.
• Data Access: SQLAlchemy v2.0 & Advanced Alchemy (Repository & Service patterns).
• Data Layer: PostgreSQL (Query Optimization), PgBouncer, Alembic, Valkey (Redis fork).

Infrastructure & Quality

• Environment: Linux (Debian 12), Angie (Nginx fork).
• DevOps & CI/CD: Docker & Docker Compose, GitHub Actions, Bash, Dependabot.
• Standards & Tooling: Pytest, Ruff, Conventional Commits, ADR.

• IronTrack — Experimental async sandbox for evaluating architectural patterns under hardware constraints.

• Optimization Context: Conducted on constrained HDD-based storage and Piledriver-based architecture to simulate high-latency, resource-limited environments.

• Key Improvements:

  • Migrated JWT signing (RSA-2048 to Ed25519) and offloaded Argon2id hashing to ThreadPoolExecutor to prevent Event Loop starvation.
  • Implemented local JTI caching for Access Tokens and native msgspec.json serialization for latency-critical paths.

• Results: Achieved up to 39% latency reduction across key authentication endpoints, verified through iterative load testing.

• Documentation: Detailed methodology, flame graphs, and performance metrics are available in the Load Testing Report.