О себе
Results-driven Software Engineer with 2+ years of hands-on experience spanning backend development, machine learning pipelines, and full-stack applications. Demonstrated track record of designing robust architectures and microservices using Python (FastAPI) and Java, alongside deploying deep learning and ML models. Thorough understanding of system workflows, asynchronous processing, and database optimization. Experienced in frontend integration (React) and end-to-end testing across staging environments. Strong focus on infrastructure cost reduction and clean, scalable code.
Опыт работы
ABAI-IT VALLEY
FullStack Developer
Developed and refactored core REST API endpoints for the university ecosystem (abu.edu.kz), reducing query processing time by ~25% through efficient database filtering. Optimized slow-running SQL queries and added missing database indexes, improving read-heavy endpoint performance by ~35% in staging environments. Assisted in implementing JWT token-based authentication and endpoint access control (RBAC), ensuring secure data flow between services. Engineered robust server-side request validation using Pydantic, dropping invalid API payloads by ~40% and preventing downstream runtime errors. Maintained code quality via structured Git workflows and authored internal API documentation (Swagger/OpenAPI), reducing teammate integration time by ~30%.
- Reduced query processing time by ~25% through efficient database filtering
- Improved read-heavy endpoint performance by ~35% in staging environments
- Dropped invalid API payloads by ~40% using Pydantic validation
- Reduced teammate integration time by ~30% via API documentation
Проекты
BarberHub — booking SaaS with RAG, ML system
Async Backend & Auth: Built a FastAPI backend with JWT, Google OAuth, RBAC, and Redis-backed rate limiting. Latency Optimization: Cut registration/upload latency by ~95% (from seconds to <100ms) by offloading SMTP and image processing to Celery workers. Infra Reduction: Reduced stateful infrastructure by 50% (2 datastores → 1) by migrating RAG vector store from ChromaDB to PostgreSQL (pgvector with HNSW). Cost Elimination & Caching: Reduced embedding API costs to $0 via self-hosted BGE-M3 models; built a local retrieval cache to skip redundant vector searches. Quota Management: Tripled (3×) effective LLM quota under strict rate limits using a custom round-robin rotation across multiple Gemini API keys. DB Optimization: Prevented sequential scans on table joins by indexing 12 non-default FK columns; cached user lookups in Redis (60s TTL) to remove DB queries. ML Classifier: Trained a scikit-learn Random Forest model via GridSearchCV, achieving 88.9% accuracy and an 89.0% F1-score for skill-grade prediction. DevOps: Containerized the ecosystem with Docker Compose and automated testing/builds via GitHub Actions.