Ibras Gataev

Backend • Middle
Node JS
Python
API Design: 6/10
Data Layer & Database: 6/10
System Architecture: 6/10
Active 18 days ago
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Overview
Technical skills
Roles

Overview

A pragmatic backend-and-tooling engineer working at a lower-senior level who produces well-tested domain services and useful developer tooling. The strongest proven skill is building robust application services with clear domain invariants and test coverage - evidenced by product/backend/src/modules/catalog/admin_service.py and product/backend/src/modules/catalog/tests/test_admin_catalog.py. Public code lacks production-grade CI/CD pipelines, Kubernetes/GitOps manifests, explicit SLO/alerting configs and infra-as-code with remote state, so platform/ops artifacts are not evidenced.

Technical skills

Node JS• Middle
Python• Middle
Node JS
Bcrypt
Python
FastAPI
SQLAlchemy
Alembic
Asyncio
Celery
Pydantic
HTTPX
Uvicorn
Boto3
structlog
aiogram
Databases
PostgreSQL
Redis
pgvector
AI/ML
LLM
AI/ML
DevOps
Prometheus
Docker Compose
Containers
Middle Backend Developer Confidence: High API Engineer
A backend-focused senior-level engineer with strong API and service-layer experience. The strongest proven skill is designing backend domain modules and reliable async APIs - evidenced by the modular routes/services/repository pattern and tests in files such as ibras0696/crm_ai/backend/src/modules/ai/routes.py and ibras0696/crm_ai/backend/src/infrastructure/uow.py. There is less public evidence of large-scale cloud infrastructure ownership (Kubernetes/operator-level integrations) or measured capacity planning / load-test results.
API Design
6/10
How well APIs are designed
API design shows consistent error envelope, pagination and idempotency patterns, and deliberate route/service layering with middleware handling (validation, rate limit, CSRF). Versioning strategy is not explicit but API contracts and schemas are present and tested.
Evidence
ibras0696/crm_ai/backend/src/common/pagination.py ibras0696/crm_ai/backend/src/middleware/error_handler.py ibras0696/crm_ai/backend/src/modules/ai/tests/test_ai_chat_api_mocked.py
Data Layer & Database
6/10
Working with databases
Substantial DB work is evidenced - async SQLAlchemy usage, a long alembic migration history, UnitOfWork abstraction and repositories; transactional boundaries and explicit locking are present. There are also direct SQL/text queries in repositories for complex operations.
Evidence
ibras0696/crm_ai/backend/alembic/versions/0001_initial_schema.py ibras0696/crm_ai/backend/src/infrastructure/uow.py ibras0696/product/backend/src/modules/catalog/graph_repository.py
Scalability & Performance
5/10
Handling load and speed
Scalability and performance patterns are present: cache layer with invalidation helpers, Celery for background work, Redis client, Prometheus metrics and async DB engines. There is less evidence of measured load-testing results or documented before/after optimizations.
Evidence
ibras0696/crm_ai/backend/src/infrastructure/cache.py ibras0696/crm_ai/backend/src/infrastructure/celery_app.py ibras0696/crm_ai/backend/src/infrastructure/metrics.py
System Architecture
6/10
Overall system structure
Clear modular architecture and domain separation (modules/services/repositories/schemas), config contract usage and import-layer checks show deliberate boundaries. The codebase is monorepo-style with well separated modules rather than microservices-for-show.
Evidence
ibras0696/job_search/backend/pyproject.toml (importlinter config) ibras0696/crm_ai/backend/src/config_contract.py ibras0696/crm_ai/backend/src/modules/* (consistent modules/services/repository structure)
Security & Auth
6/10
Protecting data and access
Authentication and authorization are implemented with session/refresh token flows, password hashing, CSRF and security headers, masked runtime secrets and encryption helpers. Input validation via pydantic and tests for auth flows further demonstrate security awareness.
Evidence
ibras0696/crm_ai/backend/src/modules/auth/security.py ibras0696/crm_ai/backend/src/middleware/csrf.py ibras0696/crm_ai/backend/src/common/runtime_secret.py
Reliability & Observability
6/10
Stability and monitoring
Good observability and reliability practices: structured logging, correlation IDs, Sentry integration, custom Prometheus metrics and middleware, plus retryable Celery task base classes and greaceful lifespan hooks. Retry/backoff specifics exist for worker tasks but full circuit-breaker configurations are not apparent everywhere.
Evidence
ibras0696/crm_ai/backend/src/infrastructure/logging.py ibras0696/crm_ai/backend/src/middleware/correlation.py ibras0696/crm_ai/backend/src/infrastructure/celery_base.py
Expertise
Backend AI & LLM• Middle
Databases & Vector Storage• Middle
Messaging & Real-time• Middle
Microservices & API Architecture• Middle
Python• Middle
Technologies
Node JS• Middle
Redis
pgvector
Prometheus
Bcrypt
Asyncio
Uvicorn
Boto3
structlog
Recommendations
  • Lead development of API-first services that require complex domain logic and async workflows (auth, billing, AI orchestration, background workers).
  • Implement AI-enabled product features (chat, document generation, vacancy screening) where LLM adapters, prompt management and token idempotency are needed.
  • Design and evolve database schemas and migrations for multi-tenant SaaS modules, including advanced repository queries and transactional UoW patterns.
  • Build observability and operational playbooks around Celery workers, Redis locks and Prometheus metrics to raise SRE maturity.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
crm_ai job_search product
Middle DevOps Engineer Confidence: Medium Generalist
A pragmatic backend-and-tooling engineer working at a lower-senior level who produces well-tested domain services and useful developer tooling. The strongest proven skill is building robust application services with clear domain invariants and test coverage - evidenced by product/backend/src/modules/catalog/admin_service.py and product/backend/src/modules/catalog/tests/test_admin_catalog.py. Public code lacks production-grade CI/CD pipelines, Kubernetes/GitOps manifests, explicit SLO/alerting configs and infra-as-code with remote state, so platform/ops artifacts are not evidenced.
CI/CD Pipelines
Automated build and deploy
Not evidenced in public code
Infrastructure as Code
Managing servers with code
Not evidenced in public code
Containerization & Orchestration
Working with containers
Not evidenced in public code
Observability & Monitoring
Watching system health
Not evidenced in public code
Reliability & Incident Response
3/10
Keeping systems up
Application-level reliability practices are evidenced - clear domain invariants, idempotency/version checks, unit and integration tests that exercise error and concurrency cases; however runbooks, deploy strategies, chaos experiments and DR rehearsal artifacts are not present.
Evidence
product/backend/src/modules/catalog/tests/test_admin_catalog.py: extensive unit tests for versioning, publish invariants and error cases
product/backend/src/modules/catalog/admin_service.py: service layer implementing version checks, SourceRequiredError and audit hooks
Cloud & Cost Optimization
Smart use of the cloud
Not evidenced in public code
Expertise
Site Reliability Engineering• Middle
Observability & Monitoring• Middle
Industries
Software• Middle
Technologies
Containers
Docker Compose
Pydantic
Alembic
aiogram
Recommendations
  • Lead development of backend domain services and business-logic APIs (FastAPI + SQLAlchemy) where rigorous invariants and test suites are required.
  • Implement internal developer tools or utilities (design-system generator, CLI helpers, data-seed tooling) that extend the existing design_system and seed tooling.
  • Hardening and operationalizing the service: add CI/CD workflows, release gates, SLOs and observability-as-code (Prometheus/Alertmanager/Grafana) to move the project to production readiness.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
product p2c_mech fastapi-order-management
Intern AI/ML Engineer Confidence: Low Generalist
A backend-focused engineer at an early-career / junior level with broad full-stack backend exposure and AI-integration interests. The strongest proven capability is building conventional web backend scaffolding and integrations as evidenced by dependency manifests and many framework-specific modules and tests (FastAPI, async SQL layers, Celery, httpx, extensive test/fixtures declarations). There is no clear public evidence of human-authored custom ML training, low-level model engineering, or production LLM-efficiency work in the human-authored files provided, which limits conclusions about advanced ML or systems design skills.
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
Technologies
AI/ML
Python• Middle
PostgreSQL
SQLAlchemy
FastAPI
LLM
Celery
HTTPX
Recommendations
  • Assign to build and extend FastAPI-based backend services and API endpoints (auth, billing, notifications) where clear framework patterns are already present.
  • Work on integrations and glue logic - adding or improving connectors, credential handling, and HTTP client adapters (httpx) with robust error handling and tests.
  • Help implement and harden background jobs and operational tasks (Celery tasks, migrations, seeding, health checks) and write end-to-end tests for these paths.
  • Start contributing to AI/LLM integration layers (adapter wiring, prompts, provider selection) under senior guidance rather than to custom model training or inference optimization.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
crm_ai job_search p2c_mech