Senior Python developers, ready in two weeks
Engineers who build production-grade Python services, not just scripts. FastAPI, Django, AI integration, data pipelines. They know the difference between code that works and code that works in production.
Tell us the role. Shortlisted profiles within 5 business days.
From web APIs to AI pipelines, the full picture
We place engineers who treat Python as a production language, not a scripting tool. Typed, tested, deployed, and monitored.
API development with FastAPI and Django
FastAPI for modern async APIs, Django for mature applications with admin interfaces and ORM. Engineers who choose based on requirements, not personal preference.
- FastAPI with Pydantic v2 for request and response validation - automatic OpenAPI docs, async by default, type-safe throughout
- Django with DRF for applications that need the full stack - admin panel, ORM, auth, and a large ecosystem of proven packages
- Async-aware code with asyncio - not blocking the event loop with synchronous operations disguised as async functions
- SQLAlchemy or Django ORM used correctly - N+1 queries identified and fixed, migrations managed without breaking production data
AI and ML integration
OpenAI API, LangChain, HuggingFace, vector databases. Engineers who build AI features that solve real problems rather than just wrapping an API call in a function.
- LLM integration with proper prompt management - system prompts version-controlled, model responses validated, fallbacks in place when the API is slow or fails
- RAG pipelines with vector databases (Pinecone, pgvector, Weaviate) - chunking, embedding, and retrieval designed for the actual content, not copied from a tutorial
- LangChain and LlamaIndex for orchestration where appropriate - knowing when to use the framework and when a direct API call is cleaner and more predictable
- Cost and latency monitoring for AI features - token usage tracked per request, caching applied where responses are deterministic, fallbacks for quota limits
Data processing and pipelines
pandas, NumPy, Polars, Apache Airflow. Engineers who treat data pipelines as production software, not as scripts that run once and are never touched again.
- pandas and Polars for tabular data processing - Polars preferred for large datasets where pandas memory usage becomes a problem
- Airflow or Prefect for pipeline orchestration - tasks retried on failure, dependencies declared explicitly, monitoring built in from the start
- Data validation with Pydantic or Great Expectations - bad data caught at the pipeline boundary, not discovered three days later in a downstream report
- ETL pipelines that handle schema changes gracefully - new columns, renamed fields, and null values handled without the pipeline silently producing wrong output
Background tasks and queues
Celery, RQ, ARQ. Async task processing that works reliably under load, with proper error handling and visibility into what is actually happening.
- Celery with Redis or RabbitMQ broker - task routing, priority queues, and retry logic configured deliberately rather than using defaults that do not fit the workload
- Dead-letter handling for tasks that keep failing - failed tasks captured, errors logged with context, and a process for human review
- Task monitoring with Flower or custom dashboards - queue depth, worker health, and task throughput visible without digging through log files
- Scheduled tasks that do not accumulate - cron jobs that check whether a previous run is still active rather than spawning duplicate processes
Testing
pytest, factory_boy, httpx. Tests that are fast, reliable, and actually catch bugs rather than passing green while production is broken.
- pytest with fixtures for test data setup - factories over raw dicts, database state cleaned between tests, no shared mutable state between test cases
- Integration tests against a real test database - not mocking the ORM, because most bugs live in the database interaction layer
- API tests with httpx or Django test client - full request-response cycle tested including authentication, authorization, and error responses
- Coverage tracked but not worshipped - 100% coverage with trivial tests is worse than 75% coverage on the code that actually matters
Deployment and infrastructure
Docker, AWS, GitHub Actions. Engineers who deploy what they build and know what to do when something goes wrong in production.
- Docker with multi-stage builds - production images small and without development dependencies, local and production environments identical
- AWS services: Lambda for serverless, ECS for containerized services, RDS for managed databases - right service for the workload
- GitHub Actions or GitLab CI pipelines - tests and lint on every PR, deploys triggered automatically on merge, rollback tested before it is needed
- Structured logging with structlog or standard logging - every log line has context (request ID, user ID, service name) so production incidents can be traced
How to hire through Oberig
- 01
Tell us the role
Seniority, stack specifics, your team setup, timezone requirements. A 30-minute call covers everything we need.
- 02
Profiles in five days
We shortlist from our vetted network. You get honest assessments, not a CV dump that makes everyone sound equally impressive.
- 03
One interview
You interview once. We handle the screening so you are not spending hours per week on mismatches.
- 04
Start in week two
Developer joins your Slack, clones the repo, picks up a ticket. No extended handholding required.
- 05
Monthly check-in
We stay in the loop to make sure the collaboration is working from both sides.
Common questions
What experience level do you place?
Mid-level (2–4 years) and senior (4–8 years). We do not place juniors for team extension. The onboarding cost outweighs the rate difference.
FastAPI or Django, which do your engineers prefer?
Both. FastAPI for greenfield APIs and microservices where async and type safety matter. Django where the admin interface, ORM, and mature ecosystem are worth the overhead. Engineers we place can work in either.
Can they handle the AI and ML parts of a Python project?
Yes, for integration work: OpenAI API, LangChain, RAG pipelines, vector databases. For pure model training and ML research, that is a different profile and we would discuss requirements separately.
Do they know data engineering tools like Airflow?
Several do. For teams that need pipeline orchestration, ETL work, or scheduled data processing, we can filter for that background specifically.
What is the notice period to stop?
30 days written notice. Most clients stay longer, but we understand business priorities change.
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