Nearshore Python Development

We offer three engagement models: staff augmentation (individual developers join your team), dedicated Python teams (a full squad managed by Tecla), and full project delivery (we build it end-to-end). Most clients start with staff aug to fill specific gaps, a Django backend engineer, a data scientist, or a DevOps engineer with Python automation experience.

Hiring nearshore Python developers allows for effective collaboration with professionals in your time zone, fluent in English, and experienced in delivering high-quality software solutions.

Python developers typically join standups and start reviewing code in week one. They push their first meaningful commits by week two. This assumes you've already onboarded them properly, access to repos, documentation, and a clear first task. The faster you treat them like a regular hire, the faster they become productive.

Our developers are skilled in Python frameworks such as Django and Flask, as well as in data science libraries like Pandas, NumPy, and TensorFlow, ensuring they can handle a wide range of development needs.

Nearshore Python teams offer significant cost savings, improved communication, and easier collaboration due to shared time zones and cultural alignment.

Individual developers integrate into your existing engineering team. You manage them, they report to your engineering lead, they work your processes. A dedicated Python development team operates as a standalone unit with their own lead and sprint cadence. They still sync with your team, but they're managed by Tecla. Choose individual developers when you have strong internal technical leadership. Choose a dedicated team when you need a self-managed unit to own a specific product or feature area.

The cost varies depending on experience and project requirements. Hiring nearshore Python developers typically costs 30-50% less than hiring locally in the U.S. Visit our Pricing page for more details.

We've placed Python developers with production experience in Django, Flask, FastAPI, Pandas, NumPy, TensorFlow, PyTorch, and scikit-learn. If you need something more niche, like Qdrant integration or custom ML pipelines, tell us the specific technical requirements upfront. We'll find someone who's actually built it, not someone who's willing to learn it on your time.