Vladislav Krikunov
Python Developer
SQL
Python
Active 8 days ago
+7 (978) 9995781 Invite to interview
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Overview
Technical skills
Timeline
Roles
Overview
A backend and Telegram-bot developer at a solid junior-to-middle level (tier 3.0) who independently builds async DB-backed services and simple CLI apps. The strongest proven skill is building async data access layers and domain validation - evidenced by BaseDAO (CollegeAPI/app/dao/base.py), StudentDAO (CollegeAPI/app/students/dao.py) and Pydantic validators (CollegeAPI/app/students/schemas.py). There is limited evidence of mature test infra, CI integration, contract or performance testing in public code.
Technical skills
SQL
Python• Middle • 3y+
Python
Celery
aiogram• 3y+
Alembic• 3y+
Asyncio• 3y+
FastAPI• 3y+
Pydantic• 3y+
SQLAlchemy• 3y+
HTTPX
Databases
Redis
PostgreSQL• 3y+
AI/ML
LLM
DevOps
Git
Rest API
Debian
Docker• 3y+
Nginx• 3y+
Ubuntu• 3y+
QA
Pytest• 3y+
Timeline
Python Backend Developer
•
Middle
Kalipso
•
Full-Time
Developed a backend and Telegram bot for a local coffee shop order system. Implemented an async FastAPI + PostgreSQL backend with SQLAlchemy Async, Alembic migrations, and SSE-based real-time admin updates. Built a Telegram client using aiogram for menu browsing, cart creation, order placement, and status notifications. Containerized the service with Docker, deployed on Ubuntu, and added pytest coverage for key endpoints and business logic.
Python
FastAPI
Asyncio
aiogram
Pydantic
PostgreSQL
SQLAlchemy
Alembic
Pytest
Docker
Ubuntu
Nginx
Python Backend Developer
•
Middle
Kasha Soft
•
Full-Time
Built a web application for order and customer management using an async FastAPI REST API. Optimized PostgreSQL access with SQLAlchemy ORM by adding indexes and removing N+1 queries using eager loading. Implemented async processing to improve API throughput and added unit and integration testing with pytest to reduce regressions. Added Pydantic validation, JWT-based authentication with role-based access, Alembic migrations, and Dockerized production deployment.
Python
FastAPI
Asyncio
Pydantic
PostgreSQL
SQLAlchemy
Alembic
Pytest
Docker
Python Backend Developer
•
Middle
FamilyTeam
•
Full-Time
Developed an HR platform for automated resume analysis and candidate management. Built a multi-stage candidate scoring pipeline combining deterministic checks and an LLM-based final score. Implemented integrations with HeadHunter, internal systems, payments via T-Bank, and interview event creation via Google Calendar, including webhook handling and idempotency. Designed a multi-tenant architecture with granular RBAC enforced through FastAPI dependencies and improved dashboard query performance with SQLAlchemy Core.
Python
FastAPI
Asyncio
Pydantic
PostgreSQL
SQLAlchemy
Alembic
HTTPX
Docker
Middle AI/ML Engineer
Confidence: Medium Generalist
A backend-focused developer with a middle-level skill set who primarily builds web APIs and service integrations. The strongest proven skill is building asynchronous Python web services using FastAPI and SQLAlchemy, evidenced by structured routers, DAO patterns, async DB engine setup, and Celery task definitions. There is little to no public evidence of machine learning, experiment tracking, custom model training, or formal test coverage and security-hardening artifacts.
Model Architecture & Training
How well models are designed and trained
Not evidenced in public code
Data Pipeline & Feature Engineering
How data is prepared for models
Not evidenced in public code
Experimentation & Evaluation
How results are measured and tested
Not evidenced in public code
MLOps & Deployment
How models are shipped to production
Not evidenced in public code
Computational Efficiency
How efficiently computing resources are used
Not evidenced in public code
Research Depth & Innovation
Depth of research and new ideas
Not evidenced in public code
Industries
Travel & Tourism• Middle
Technologies
Python• Middle • 3y+
SQL
Redis
Rest API
SQLAlchemy• 3y+
Debian
FastAPI• 3y+
Git
Docker• 3y+
Ubuntu• 3y+
Nginx• 3y+
LLM
Celery
HTTPX
Alembic• 3y+
Recommendations
- Develop backend REST/async APIs and booking/inventory services using FastAPI and SQLAlchemy.
- Implement and extend asynchronous task pipelines and integrations with Celery and Redis for background jobs like email processing and image handling.
- Build CRUD-heavy admin dashboards and data models with Alembic migrations and ModelView-style admin layers.
- Improve engineering maturity by adding unit/integration tests, documented error handling, and CI/CD for deployments.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Backend Developer
Confidence: Medium API Engineer
A backend engineer at a Middle level who produces working async Python services with database migrations and DAO-layer SQL logic. The strongest proven skill is database-backed API logic, evidenced by the Alembic migrations with computed columns and the BookingDAO CTE and availability-check query (vladick5513/Hotel_reservations/migrations/versions/5db7614b3328_create_rooms_users_bookings.py and vladick5513/Hotel_reservations/app/bookings/dao.py). There is limited evidence of production-grade security, comprehensive observability, or advanced scalability patterns in the human-authored files reviewed.
API Design
2/10
How well APIs are designed
Minimal API-design artifacts in the human-authored files - no explicit versioning, idempotency, or pagination design present there.
Data Layer & Database
4/10
Working with databases
Concrete DB work is present - Alembic migrations, computed columns and a CTE-based availability query using SQLAlchemy async sessions, but limited evidence of explicit transaction/isolation tuning or index/optimization notes.
Scalability & Performance
2/10
Handling load and speed
Some async DB usage and a CTE query indicate awareness of performance, but there is no observable caching/invalidation, queue-based decoupling, or measured optimization in the human-authored files.
System Architecture
2/10
Overall system structure
Basic module separation (DAOs, migrations, models), but not enough evidence of deliberate multi-service decomposition, graceful degradation, or config/secret management decisions in the analyzed human-authored files.
Security & Auth
1/10
Protecting data and access
Very little security/auth logic in the human-authored files reviewed; no observable token lifecycle, role-based checks or input-boundary hardening in those files.
Reliability & Observability
3/10
Stability and monitoring
Some reliability signals - use of Alembic migrations and try/except patterns plus a logger import show attention to operational concerns, but no comprehensive observability, retries with backoff, or circuit-breaker patterns in the human-authored files.
Expertise
Python• Middle
Microservices & API Architecture• Middle
Messaging & Real-time• Junior
Industries
Education• Middle
Food & Beverages• Middle
Travel & Tourism• Middle
Technologies
PostgreSQL• 3y+
Recommendations
- Assign to build or extend REST/HTTP APIs using FastAPI + async SQLAlchemy where database schema evolution via Alembic and DAO-layer logic is required.
- Use for implementing and hardening booking/order business logic - availability checks, computed-cost logic and associated integration tests.
- Have them add operational improvements: explicit transaction boundaries, idempotency for webhooks/endpoints, and structured observability (correlation IDs, metrics).
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle QA Engineer
Confidence: Medium Generalist
A backend and Telegram-bot developer at a solid junior-to-middle level (tier 3.0) who independently builds async DB-backed services and simple CLI apps. The strongest proven skill is building async data access layers and domain validation - evidenced by BaseDAO (CollegeAPI/app/dao/base.py), StudentDAO (CollegeAPI/app/students/dao.py) and Pydantic validators (CollegeAPI/app/students/schemas.py). There is limited evidence of mature test infra, CI integration, contract or performance testing in public code.
Test Automation Frameworks
3/10
Building automated tests
Basic unit test usage with setup/teardown and focused assertions; no test fixtures, mocks, parallelization, or test infra for isolation and no browser/E2E framework evidence.
Evidence
library_tz/tests/test_library.py: unittest.TestCase with setUp/tearDown and multiple test methods
library_tz/app/library.py: Library class methods used directly by tests (add_book, delete_book, _save_books/_load_books)
Test Coverage & Strategy
2/10
What and how to test
Some negative-path tests (invalid deletes/status) and input validation in schemas, but no risk-based test stratification, property-based tests, or mutation/coverage signals.
Evidence
library_tz/tests/test_library.py: tests for delete_nonexistent_book and test_update_status_invalid
CollegeAPI/app/students/schemas.py: pydantic field_validators for phone_number and date_of_birth
API & Integration Testing
1/10
Testing how parts work together
Application code shows integration patterns with async DB usage and DAOs, but there are no explicit API or contract tests, schema-driven contract checks, or integration testcontainers.
Evidence
CollegeAPI/app/dao/base.py: BaseDAO using async_session_factory with transactional commit/rollback patterns
StudyConnect/app/bot/crud.py: repository methods using AsyncSession and SQL queries for matching logic
Performance & Load Testing
Testing speed under load
Not evidenced in public code
Bug Reporting & Analysis
Finding and describing bugs
Not evidenced in public code
CI Test Integration
Running tests automatically
Not evidenced in public code
Expertise
Unit & Component Testing• Junior
Industries
Education• Middle
Technologies
Asyncio• 3y+
Pytest• 3y+
Pydantic• 3y+
aiogram• 3y+
Recommendations
- Develop and maintain async REST APIs or microservices that require SQLAlchemy/Alembic migrations and Pydantic validation.
- Build and extend Telegram bots and chat workflows using aiogram and Asyncio for conversational features.
- Implement unit and integration test suites (add pytest, fixtures, mock DB or testcontainers) and add CI pipelines to improve delivery quality.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: