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 the REST API for the university ecosystem (abu.edu.kz). Optimized SQL queries and implemented indexing, which enhanced the performance of read-heavy endpoints in the staging environment. Integrated JWT authentication and Role-Based Access Control (RBAC) to ensure secure access management. Implemented server-side request validation using Pydantic, significantly reducing the number of malformed payloads. Maintained high code quality standards through Git-driven workflows and documented APIs using Swagger/OpenAPI.
- 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
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.
Образование
SDU University
2023 — 2027Курсы
DataArt(Software Testing Foundation)