Offshore Python Developer

2 to 5 business days for a vetted shortlist. Total time to a signed engagement typically runs 2 to 4 weeks. How quickly you move on profiles is the main variable. The search itself is fast. We've placed Python developers in under two weeks when the client had a technical lead ready to interview and a defined job description going in. We've also seen it take five or six weeks when the internal approval process was still in progress. The pipeline is on our side. The timeline is mostly on yours.

Django and FastAPI are where we have the deepest depth, with the most verified production experience. Flask, Celery, Airflow, SQLAlchemy, and FastAPI microservices round out the backend pool. On the data and ML side, Pandas, NumPy, scikit-learn, PyTorch, and TensorFlow. Not every developer covers all of these. We match based on your specific framework stack, not general Python familiarity. If you need someone who has shipped a Celery task queue in production, that's what we'll screen for. Not someone who knows what Celery is.

Short answer. It depends on the depth you need. We can place Python engineers with solid ML experience, training and evaluating models with scikit-learn or PyTorch, deploying inference APIs via FastAPI, building feature pipelines with Airflow. What's harder to find offshore (and onshore, frankly) is a dedicated ML researcher designing novel model architectures from first principles. If you need someone to build and ship ML features into production, yes. If you need someone who reads arxiv papers on weekends and publishes original research, that's a different conversation.

NDA and IP assignment from the engineer before they start. Least-privilege access to repositories, meaning they access what the role requires and nothing more. Your codebase stays on your servers and in your version control system from day one. It doesn't get shared with third parties or with us. We can walk through the specifics before you sign anything. Most clients want to hear this upfront rather than after the first engagement has started. Bring it to the first call.

Usually we can course-correct before it gets to replacement. A common pattern is that early issues are onboarding-related, not performance-related. Unclear scope, missing architectural context, mismatched expectations about async communication norms. We'd rather talk through the likely failure modes before we start than manage a difficult call after week four. That said, if a placement genuinely isn't working, we support replacement. Ask us for the specifics when we talk. Don't assume. The details matter and we don't use blanket guarantees that collapse under scrutiny.

Couple of reasons this works for startups and one reason it doesn't. It works when you have a technical co-founder or CTO who can run the onboarding, own the code review, and define what good looks like. It doesn't work when you're pre-product and need someone who can also choose the stack, define the architecture, and figure out what to build. An offshore engineer is an execution resource. Without internal technical leadership, that resource doesn't have the context to be productive. If you're post-Series A with a CTO who's drowning in code review? Yes. This model was built for exactly that situation.