Maksim Maslin
Python Developer
12+ years exp
JavaScript
Python
SQL
Data Pipeline & Feature Engineering: 6/10
MLOps & Deployment: 6/10
Computational Efficiency: 4/10
Active 23 days ago
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Overview
Technical skills
Timeline
Roles
Overview
A pragmatic senior-level backend engineer focused on API-driven pipelines and integrations. The strongest proven skill is designing and implementing a clean-architecture marketplace pipeline (composition container, ports/adapters, job runner, idempotency and LLM integration) as seen in the marketplace-pipeline package. The public code does not show large-scale multi-region operations, detailed DB performance tuning at raw SQL level, or long-lived token lifecycle/revocation systems.
Technical skills
JavaScript
Python• Senior • 12y+
SQL• Junior • 7y+
Python
Asyncio
HTTPX
Pydantic
Requests
Django• 10y+
Django REST Framework• 9y+
Aiohttp• 6y+
Alembic• 6y+
Celery• 6y+
SQLAlchemy• 6y+
FastAPI• 3y+
Databases
SQLite
PostgreSQL• 9y+
RabbitMQ• 6y+
Redis• 6y+
Apache Kafka• 3y+
AI/ML
OpenAI SDK
DevOps
Azure
Grafana
Rest API
OpenTelemetry
Git• 9y+
Docker• 6y+
Kubernetes• 3y+
CI/CD
Docker Swarm
GitLab CI
Frontend
Vue.js
JQuery• 20y+
Bootstrap• 10y+
D3.js• 6y+
Cybersecurity
Keycloak
QA
Pytest• 6y+
Swagger
Timeline
Python Backend Developer
•
Middle
Osinit
•
Full-Time
Developed a REST API for event-plan activities, including CRUD operations and permission handling by company, groups, and users. Implemented workflow-driven status transitions with pre/post functions, validations, and a service layer. Added integration tests using Pytest and restored Swagger/OpenAPI documentation for stability of the CI/CD pipeline.
Python
Django
Django REST Framework
PostgreSQL
Redis
Celery
RabbitMQ
Docker
GitLab CI
Pytest
Swagger
Python Backend Developer
•
Middle
Big3
•
Full-Time
Built backend features for a B3 platform used in business and the public sector. Implemented hierarchical reference logic with depth calculation in a single SQL query and optimized recursive updates using CTEs. Fixed authentication issues with Keycloak logout flow, restored email sending via Celery queues, and improved reliability by refactoring legacy code with Pytest coverage.
Python
Django
Django REST Framework
PostgreSQL
Redis
Celery
RabbitMQ
Docker Swarm
Pytest
Python Backend Developer
•
Middle
Samolet Development
•
Full-Time
Worked on corporate IT services, developing integrations and access-control features. Implemented 2FA for an OAuth2 provider (S.ID) serving many applications and users. Built forced logout by revoking tokens and clearing Redis sessions on LDAP events, and developed an ECM access approval optimization via a JWT proxy. Created a Kafka adapter for high-throughput event processing and supported API documentation and second-line OAuth2 provider support.
Python
Django
FastAPI
PostgreSQL
Redis
Apache Kafka
Celery
Docker
Kubernetes
Pytest
Python Backend Developer
•
Middle
Globant
•
Full-Time
Developed backend functionality for Flare, an internal project for Ubisoft focused on visualizing game bugs. Migrated a large Python codebase from Python 2 to Python 3 while preserving the existing API behavior. Integrated Microsoft Azure AD authorization and increased test coverage for critical modules using Pytest.
Python
PostgreSQL
Redis
Pytestsince 2021
Python Backend Developer
•
Middle
Lendsbay
•
Full-Time
Developed backend services for a P2P lending platform supporting a high-load mobile application. Implemented parsing of BKI XML exports to enable automated credit scoring. Designed an algorithm to reduce penalties via business rules to lower customer churn, and delivered analytics dashboards in the admin area using metric filtering and visualization.
Python
Aiohttp
SQLAlchemy
Alembic
PostgreSQL
Celerysince 2020
Redissince 2020
RabbitMQsince 2020
Dockersince 2020
D3.js
Junior Python Backend Developer
•
Junior
FinTech
•
Full-Time
Built backend components for an AHO resource accounting web service. Implemented REST API endpoints and database models using Django and Django REST Framework with PostgreSQL. Contributed to the development of backend data access and API behavior for the accounting domain.
Python
Django
Django REST Frameworksince 2017
PostgreSQLsince 2017
Git
Django Developer (Freelance)
•
Middle
Freelance
•
Freelance
Delivered freelance Django development for rent accounting functionality and landing pages. Implemented UI components for landing pages using Bootstrap. Used jQuery for interactive elements on landing pages.
Pythonsince 2016
Djangosince 2016
Bootstrap
JQuery
Python Backend Developer Intern
•
Junior
Corona Travel
•
Internship
Completed a backend internship as a Python backend developer in Moscow. Supported development work related to Python-based web applications. Gained hands-on experience with backend development practices during the internship period.
Python
Senior AI/ML Engineer
Confidence: High LLM Engineer
A pragmatic senior-level backend engineer specializing in production ML/LLM integrations and reliable data pipelines. The strongest proven skill is designing and shipping resilient pipelines with clear layering and test coverage - evidenced by the Clean-Architecture pipeline, Ozon scraper (ozon_http.py) and comprehensive tests. There is no evidence of custom model training, experiment tracking, or novel research implementations in public code.
Model Architecture & Training
2/10
How well models are designed and trained
No evidence of custom model architectures or training; LLM is used via an adapter with batching, parsing and graceful fallback rather than training or custom layers.
Evidence
src/marketplace_pipeline/infrastructure/adapters/llm/openai_classifier.py
src/marketplace_pipeline/llm/classifier.py
tests/test_llm.py
Data Pipeline & Feature Engineering
6/10
How data is prepared for models
Clear, layered data/ETL pipeline and domain-driven design - scrapers, collection result model, selection service, and multiple persistence adapters (JSON/SQLite/Postgres).
Evidence
src/marketplace_pipeline/parser/ozon.py
src/marketplace_pipeline/pipeline.py
src/marketplace_pipeline/domain/services/product_selection_service.py
Experimentation & Evaluation
2/10
How results are measured and tested
Good automated test coverage and CI-oriented settings exist, but there is no experiment tracking, ablation tests or ML-focused evaluation framework present.
Evidence
pyproject.toml (pytest + coverage target >=95%)
tests/test_coverage.py
tests/test_llm.py
MLOps & Deployment
6/10
How models are shipped to production
Strong operational engineering: FastAPI service, background job runners (Celery + thread pool runner), composition/factory containers, metrics and tracing integration (OTel/Sentry) and multiple persistence/backends supported.
Evidence
src/marketplace_pipeline/interfaces/api/app.py
src/marketplace_pipeline/infrastructure/services/celery_job_runner.py
src/marketplace_pipeline/infrastructure/observability/tracing.py
Computational Efficiency
4/10
How efficiently computing resources are used
Attention to robustness and efficiency - batching of LLM calls, HTTP client with retry/exponential backoff, rate limiter and ThreadPoolExecutor-based job runner, but no GPU/quantization/low-level optimizations.
Evidence
src/marketplace_pipeline/infrastructure/http/http_client.py
src/marketplace_pipeline/infrastructure/adapters/llm/openai_classifier.py:_chunk
src/marketplace_pipeline/infrastructure/services/pipeline_job_runner.py
Research Depth & Innovation
1/10
Depth of research and new ideas
Little-to-no research depth - no paper implementations, novel algorithms, or custom layers; engineering is production-focused rather than research-oriented.
Evidence
src/marketplace_pipeline/infrastructure/http/ozon_http.py
Expertise
LLM• Senior
MLOps & Model Lifecycle• Senior
Industries
Commerce• Senior
Education• Middle
Technologies
SQL• Junior • 7y+
Rest API
SQLAlchemy• 6y+
RabbitMQ• 6y+
OpenTelemetry
Docker Swarm
GitLab CI
Azure
CI/CD
Git• 9y+
Docker• 6y+
Kubernetes• 3y+
Apache Kafka• 3y+
Grafana
Asyncio
Django REST Framework• 9y+
Aiohttp• 6y+
LLM• mentioned only
Python• mentioned only
Recommendations
- Build and maintain LLM-integrated production pipelines that require idempotency, batching and CRM integration (classification → task creation workflows).
- Implement production-grade API backends with background job orchestration, observability and scaling (FastAPI + Celery + metrics/tracing).
- Develop robust web-scraping and ingestion components with proxy/anti-bot handling and graceful degradation (Ozon client, proxy quota checks).
- Own backend features for edtech-style products requiring audited user interactions, timeouts and async ML validation (Django + Channels + Celery).
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Senior Data Scientist
Confidence: High Data Engineer
A Senior-level data engineer focused on building reliable ETL and backend systems with production concerns such as idempotency, retries, rate limiting and observability. The strongest proven skill is designing and implementing production-grade pipelines and adapters - e.g., the marketplace pipeline with Ozon parser, LLM classifier adapters, idempotency stores and comprehensive tests. There is little to no evidence of formal statistical analysis, EDA notebooks or end-to-end predictive-model training and tuning in public code.
Statistical Rigor
1/10
Correct use of statistics
Very little statistical analysis or formal inference; only a simple LLM-based classifier with validation checks exists, no hypothesis testing or uncertainty quantification.
Evidence
marketplace-pipeline/src/marketplace_pipeline/infrastructure/adapters/llm/openai_classifier.py
marketplace-pipeline/tests/test_coverage.py::test_classifier_invalid_llm_response
marketplace-pipeline/tests/test_llm.py
Data Wrangling & Cleaning
7/10
Preparing and cleaning data
Strong, production-grade data wrangling and cleaning across parsers and persistence layers with explicit handling of duplicates, invalid prices, atomic file writes and idempotency stores.
Evidence
marketplace-pipeline/src/marketplace_pipeline/infrastructure/adapters/parsers/ozon_collector.py
marketplace-pipeline/src/marketplace_pipeline/infrastructure/io/atomic.py
marketplace-pipeline/tests/test_coverage.py::test_ozon_skips_duplicate_and_invalid_price
Exploratory Analysis & Visualization
Exploring and visualizing data
Not evidenced in public code
Predictive Modeling
2/10
Building models that predict
Minimal predictive work - an LLM-based classifier wrapper and mock classifier exist, but there is no model training, CV, calibration, or error-analysis pipeline.
Evidence
marketplace-pipeline/src/marketplace_pipeline/infrastructure/adapters/llm/openai_classifier.py
marketplace-pipeline/llm/classifier.py
marketplace-pipeline/tests/test_llm.py
Business Insight & Impact
4/10
Turning analysis into business value
Clear business-driven pipeline decisions (select top premium/economy products and create CRM tasks) showing product-to-business mapping, but limited evidence of deeper metrics, cost-of-error reasoning or ROI modeling.
Evidence
marketplace-pipeline/src/marketplace_pipeline/domain/services/crm_task_factory.py
marketplace-pipeline/src/marketplace_pipeline/selectors.py
Reproducibility & Notebook Hygiene
6/10
Clean, repeatable analysis
Good reproducibility practices: comprehensive pytest suite, Makefile commands, settings validation and test coverage targets; no notebooks but strong test-driven and configuration-driven reproducibility.
Evidence
marketplace-pipeline/AGENTS.md
marketplace-pipeline/tests/test_coverage.py
marketplace-pipeline/docs/TESTING.md
Expertise
Analytics• Middle
Industries
Commerce• Senior
Data & Analytics• Senior
Education• Middle
Technologies
Python• Senior • 12y+
PostgreSQL• 9y+
Redis• 6y+
FastAPI• 3y+
OpenAI SDK
Django• 10y+
SQLite
Celery• 6y+
Pydantic
HTTPX
Requests
Alembic• 6y+
LLM• mentioned only
Python• mentioned only
Recommendations
- Develop production ETL pipelines that require resilient web scraping, proxy/anti-bot handling and downstream CRM integration.
- Implement backend services and job orchestration for data enrichment - design durable job runners, idempotency and observability.
- Build and harden analytics ingestion pipelines with robust testing, schema migration (Alembic) and DB adapters (SQLite/Postgres).
- Collaborate on edtech backend components requiring auditability and guaranteed writes (session tracking, interaction logs).
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Senior Backend Developer
Confidence: High API Engineer
A pragmatic senior-level backend engineer focused on API-driven pipelines and integrations. The strongest proven skill is designing and implementing a clean-architecture marketplace pipeline (composition container, ports/adapters, job runner, idempotency and LLM integration) as seen in the marketplace-pipeline package. The public code does not show large-scale multi-region operations, detailed DB performance tuning at raw SQL level, or long-lived token lifecycle/revocation systems.
API Design
6/10
How well APIs are designed
Clear API surface with FastAPI app, middleware for API-key auth, rate-limiting, idempotency handling and explicit request-id propagation; idempotency behaviour tested.
Evidence
marketplace-pipeline/src/marketplace_pipeline/interfaces/api/app.py: RequestIdMiddleware and app creation
marketplace-pipeline/src/marketplace_pipeline/interfaces/api/middleware/auth.py: ApiKeyAuthMiddleware
marketplace-pipeline/src/marketplace_pipeline/interfaces/api/routes/jobs.py: create_job handler and Header usage
Data Layer & Database
5/10
Working with databases
Multiple persistence adapters (SQLite, Postgres) with alembic migrations and idempotency stores; good separation via ports/adapters though few low-level tuned SQL optimizations are visible.
Evidence
marketplace-pipeline/src/marketplace_pipeline/infrastructure/adapters/persistence/sqlite_job_repository.py: schema init and CRUD
marketplace-pipeline/alembic/versions/001_initial_pipeline_jobs.py: alembic migration
marketplace-pipeline/src/marketplace_pipeline/infrastructure/adapters/persistence/postgres_job_repository.py: PostgresJobRepository implementation
Scalability & Performance
6/10
Handling load and speed
Thoughtful scalability work - thread pool job runner, optional Celery worker, rate limiter implementations, robust HTTP client with retries and an Ozon-specific HTTP client handling proxies and throttling.
Evidence
marketplace-pipeline/src/marketplace_pipeline/infrastructure/services/pipeline_job_runner.py: ThreadPoolExecutor-based runner
marketplace-pipeline/src/marketplace_pipeline/infrastructure/services/celery_job_runner.py: Celery job enqueuing adapter
marketplace-pipeline/src/marketplace_pipeline/infrastructure/http/http_client.py: HttpClient with retry logic (tenacity) and context manager
System Architecture
6/10
Overall system structure
Deliberate Clean Architecture / DDD layout, composition container and factories, clear port/adapter boundaries and feature toggles for scale backends - shows conscious architectural choices.
Evidence
marketplace-pipeline/AGENTS.md and docs/CLEAN_ARCHITECTURE.md: declared Clean Architecture + DDD
marketplace-pipeline/src/marketplace_pipeline/infrastructure/composition/container.py: Container wiring of adapters and use-cases
marketplace-pipeline/src/marketplace_pipeline/infrastructure/composition/factories.py: factories for building persistence, idempotency stores and job runners
Security & Auth
5/10
Protecting data and access
Auth and input boundaries are explicit (API key middleware, pydantic schemas); secrets and settings are modelled; token lifecycle/revocation patterns are not present but common threats (inject, replay) have mitigations like idempotency and limited surface.
Evidence
marketplace-pipeline/src/marketplace_pipeline/interfaces/api/middleware/auth.py: ApiKeyAuthMiddleware using secrets
marketplace-pipeline/src/marketplace_pipeline/interfaces/api/schemas/jobs.py: Pydantic request/response schemas
marketplace-pipeline/src/marketplace_pipeline/infrastructure/config/settings.py: Settings via pydantic/pydantic-settings
Reliability & Observability
6/10
Stability and monitoring
Good reliability and observability - structured JSON logging, correlation ids, metrics registry, tracing hooks and extensive tests asserting retries, graceful degradation and job lifecycle.
Evidence
marketplace-pipeline/src/marketplace_pipeline/infrastructure/logging/context.py and setup.py: correlation id and JSON formatter
marketplace-pipeline/src/marketplace_pipeline/infrastructure/observability/metrics.py: MetricsRegistry with Redis-backed counters and Prometheus rendering
marketplace-pipeline/src/marketplace_pipeline/infrastructure/observability/tracing.py: OpenTelemetry / Sentry setup hooks
Verified artifacts
Expertise
Python• Senior
Microservices & API Architecture• Senior
Backend AI & LLM• Middle
Messaging & Real-time• Middle
Recommendations
- Build API-first ETL and automation services that integrate external marketplaces with CRMs and LLMs - lead design and implementation of port/adapter patterns and background job orchestration.
- Implement production-grade job orchestration and scaling (Celery / Kubernetes workers, observability and SLOs) for pipelines that require graceful degradation and idempotent external side-effects.
- Develop backend services requiring robust HTTP clients, proxy/throttling strategies and anti-bot workarounds (web parsers and resilient HTTP layers).
- Own backend API contracts and middleware (auth, rate-limit, request-id propagation, structured logging and metrics) for small-to-medium microservice teams.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: