Backend
Python
Python Engineers for AI, APIs & Data Pipelines.
Production Python engineering across machine learning, API development, and data infrastructure. Our developers build FastAPI services, orchestrate ML pipelines, and engineer ETL systems that process millions of records daily. We serve data and engineering teams across the United States, Germany, Netherlands, United Kingdom, and Scandinavia, with 4–6 hours of daily CET overlap and fully GDPR-compliant data handling.
Use Cases
What we build with Python.
Machine Learning Pipelines
End-to-end ML workflows with scikit-learn, PyTorch, or Hugging Face — from feature engineering and model training to serving predictions via REST or gRPC. Model versioning with MLflow, experiment tracking, and automated retraining. Built ML systems for healthtech companies in Munich, predictive analytics platforms in New York, and recommendation engines for e-commerce brands in Amsterdam.
FastAPI Microservices
High-performance async APIs with FastAPI, Pydantic validation, and auto-generated OpenAPI docs. Dependency injection, middleware chains, and background task workers — all type-hinted and testable. Delivered microservice architectures for logistics platforms in Rotterdam, fintech APIs in London, and B2B SaaS backends across the US market.
Data Engineering & ETL
Robust data pipelines with Apache Airflow, Dagster, or Prefect orchestrating extraction from APIs, databases, and file systems. Transformation layers using Pandas, Polars, or dbt, loading into Snowflake, BigQuery, or Redshift. Built pipelines processing 50M+ records daily for analytics companies in Berlin and data platforms in Chicago.
LLM Integration & AI Agents
OpenAI, Anthropic, and open-source LLM integration into production applications. RAG pipelines with vector databases, prompt engineering, function calling, and agent frameworks like LangChain or custom orchestrators. Deployed AI-powered products for legal tech in Frankfurt, customer support automation in Stockholm, and content platforms across the US.
Scientific Computing & Simulation
NumPy, SciPy, and custom numerical solvers for computational finance, biostatistics, and engineering simulation. Jupyter-based research environments that transition to production code with proper packaging and testing. Served quantitative teams in Zurich, research groups in Cambridge, and engineering firms in Houston.
Automation & Scripting Infrastructure
Python-powered automation for DevOps, data collection, and business process workflows. Web scrapers with Playwright and Scrapy, scheduled jobs with Celery, and Slack/Teams integrations for alerting. Built automation suites for operations teams in Madrid, marketing agencies in Paris, and infrastructure teams across the US East Coast.
Expertise
How we work with Python.
Async Python & High-Performance APIs
We build on asyncio, uvicorn, and ASGI middleware to handle thousands of concurrent connections. Connection pooling with asyncpg, background task queues, and structured concurrency patterns. Profiling with py-spy and memory analysis with memray to eliminate bottlenecks in production workloads.
ML Engineering & Model Serving
Beyond training notebooks — we productionize models with proper feature stores, model registries, and serving infrastructure. ONNX Runtime or TorchServe for inference, A/B testing frameworks for model comparison, and monitoring for drift detection. EU-deployed model serving with data residency guarantees when required.
Type-Safe Python with Pydantic
Strict type hints enforced by mypy or pyright across the entire codebase. Pydantic models for validation at every boundary — API inputs, configuration, database rows, and external service responses. Settings management with pydantic-settings for environment-specific configuration across development, staging, and EU production environments.
Packaging & Dependency Management
Modern Python tooling with uv or Poetry for dependency resolution, virtual environments, and lockfiles. Monorepo support with namespace packages and editable installs. Docker images optimized with multi-stage builds and layer caching — slim production images under 200MB for fast container startup in any deployment region.
Testing & Observability
Pytest with fixtures, parametrize, and factory-based test data. Integration tests against real databases using testcontainers. Structured logging with structlog, distributed tracing with OpenTelemetry, and metrics exported to Prometheus or Datadog. Full observability stack for debugging production issues across US and EU deployments.
Why us
Why TBI for Python.
Fast Onboarding, Deep Context
Our Python engineers have production experience across FastAPI, Django, ML pipelines, and data engineering. They review your codebase, understand your architecture, and open their first PR within 2–3 days — no months of handholding required.
AI-Augmented Development
Our engineers use Cursor, Copilot, and LLM-powered tools for code generation, docstring writing, and test scaffolding. Python's dynamic nature benefits enormously from AI assistance — our developers pair AI speed with type-hint discipline to deliver faster without sacrificing code quality.
US & EU Timezone Overlap
Working from IST (UTC+5:30), we overlap 4–6 hours with CET and 3–4 hours with US Eastern. Pipeline failures get triaged before your Berlin data team starts their day. Model training results are reviewed and iterated on during shared hours with your New York ML team.
GDPR-Compliant Data Handling
Python data pipelines handling EU citizen data follow GDPR by design — data anonymization utilities, consent-aware processing, and EU-region storage. We sign DPAs, deploy to eu-central-1 or eu-west-1, and implement data retention policies directly in pipeline code.
Related
Our Python teams often ship with.
FAQ
Common questions.
How much does it cost to hire a dedicated Python developer offshore?
Full-time Python engineers start at $5,000/month. Senior engineers with ML, data engineering, or async API specialization range from $6,500–$9,500/month. A senior Python developer in the US commands $150,000–$200,000/year; in the EU, €80,000–€130,000/year. Our model delivers equivalent expertise — including ML and data pipeline skills — at 60–70% lower cost with the same working-hour availability.
How fast can a Python developer start working on our project?
2–3 days from contract to first PR. We match developers to your specific Python ecosystem — your web framework, ML stack, data tools, and deployment setup. They arrive with your repo cloned, dependencies installed, and an understanding of your architecture. The first contribution is always something tangible, not a setup task.
Can your Python developers handle both ML engineering and backend API development?
Yes — many of our senior Python engineers work across both domains. They build FastAPI services that serve ML model predictions, design feature pipelines that feed into training jobs, and implement monitoring for model performance in production. For larger engagements, we can also provide specialists focused on one domain.
How do you handle GDPR compliance in Python data pipelines?
At the code level: anonymization functions for PII fields, consent-flag checks before processing, and configurable data retention in pipeline orchestrators. At the infrastructure level: EU-region compute and storage, encrypted data at rest and in transit, and audit logging for data access. We sign DPAs and can deploy entirely within EU boundaries.
What timezone overlap do your Python engineers have with US and EU teams?
Our team operates from IST (UTC+5:30). That means 4–6 hours overlapping with CET — ideal for afternoon pairing sessions or sprint ceremonies with European teams — and 3–4 hours with US Eastern for morning syncs. Data pipeline monitoring happens around the clock, with handoffs that ensure issues are caught within hours, not days.
Ready to scale your
Python team?
Tell us what you need. We'll scope the engagement and match you with Python engineers in days.