Hire Python developers
Senior Python engineers embedded in your team within days, with a fractional CTO reviewing their output every week.
Python engineering across backend, data, and automation
Python runs your API layer, your data pipelines, your automation scripts, and half your internal tooling. The range of the language means the range of the developer matters. Mavric places senior Python engineers who have built production Django applications, designed FastAPI microservices, orchestrated ETL pipelines with Celery and Airflow, and shipped ML-serving infrastructure on AWS and GCP.
Every Mavric Python developer completes a technical assessment tailored to your stack before they reach your interview. We evaluate real production skills: API design, ORM optimization, async patterns, testing discipline, and infrastructure awareness. You interview one strong candidate instead of sorting through dozens of generalists who list Python on a resume.
Why teams hire Python developers through Mavric
Pre-vetted for Python, not just scripting
We screen hundreds of candidates for every role. Each Python developer is assessed on framework depth (Django, Flask, FastAPI), async programming, database optimization, and production deployment practices before you see a single resume.
CTO oversight from day one
A fractional CTO is assigned to your account the moment your developer starts. They review code quality, flag architectural risks, and deliver a weekly performance report to your inbox every Monday.
$6K-$8K/mo with a 30-day guarantee
Senior Python engineers at 40-50% less than US contractors. If a placement does not meet expectations due to performance or timezone issues within 30 days, we replace them at no extra cost. Under 5% of placements ever need it.
Technical expertise
Mavric Python developers work across the full Python ecosystem. From web frameworks to data infrastructure, they bring production experience in the tools your team already uses.
Mavric developers built Tablecloth's impact management platform v2 from the ground up using NestJS, Next.js, and PostgreSQL. The project delivered in three months with a Python-backed data processing layer handling ESG reporting calculations across hundreds of client organizations.
Read the case study