Hire Offshore Machine Learning Engineers

Our Machine Learning candidates go through model-building assessments — not just theory questions. We test data pipeline architecture, feature engineering judgment, model evaluation methodology, and deployment readiness covering Supervised & Unsupervised Learning, Deep Learning, NLP & LLMs. Candidates demonstrate their approach to a real ML problem: data exploration, model selection, hyperparameter tuning, and production monitoring. We also verify certifications such as AWS Certified Machine Learning Specialty and Google Professional Machine Learning Engineer. We specifically filter for candidates who've shipped ML to production, not just trained models in notebooks.

All our Machine Learning developers are based in India and work schedules that provide 4-6 hours of daily overlap with US, UK, or Australian business hours. This covers standups, code reviews, pair programming, and stakeholder meetings. Complex development work happens during their extended hours, meaning you review pull requests each morning with minimal wait time. We use Python, TensorFlow, PyTorch for asynchronous collaboration and handoffs. We've optimized this cadence across hundreds of engagements.

Every engagement is covered by a comprehensive NDA, IP assignment agreement, and data security protocols. All code, designs, and deliverables created by your Machine Learning developer are your property — full IP assignment, no exceptions. Access to Python, TensorFlow, PyTorch and other client systems is managed through role-based permissions. Our infrastructure includes VPN-only access to client environments, endpoint security on all workstations, and we can accommodate SOC 2, HIPAA, or other compliance frameworks. Background verification is standard for all candidates.

We offer a free replacement guarantee. If your Machine Learning developer isn't meeting expectations, tell us and we'll source a replacement with proven expertise in Supervised & Unsupervised Learning, Deep Learning, NLP & LLMs within 5 business days at no additional cost. The transition includes a structured handover: documentation of in-progress work, codebase walkthrough with the new resource, and overlap period where both are available. The replacement will be pre-screened for experience in Predictive Analytics & Forecasting, Recommendation Systems, Fraud & Anomaly Detection. In practice, we rarely need replacements — our vetting process has a 95%+ retention rate past the first 90 days.

From your initial brief to an onboarded Machine Learning developer typically takes 8-10 business days. We deliver 3-5 pre-vetted profiles with experience in Supervised & Unsupervised Learning, Deep Learning, NLP & LLMs within 48 hours. You interview your shortlist, and once selected, onboarding covers environment setup, codebase walkthrough, tooling access, and first sprint planning. Most Machine Learning developers submit their first meaningful pull request within the first week. Our candidates are experienced in Predictive Analytics & Forecasting, Recommendation Systems, Fraud & Anomaly Detection use cases.

We offer three engagement models: (1) Dedicated Resource — a full-time Machine Learning expert specializing in Supervised & Unsupervised Learning, Deep Learning, NLP & LLMs works exclusively on your project with 40 hrs/week, daily standups, and direct communication covering areas like Predictive Analytics & Forecasting, Recommendation Systems, Fraud & Anomaly Detection. (2) Team Extension — a managed pod (2-10 people) with tech lead, developers, QA, and optional PM for sprint-aligned delivery. (3) Project-Based — fixed scope with milestone delivery, full PM oversight, and UAT. Most clients start with a dedicated resource and scale to a team as the project grows.

Your monthly rate covers the developer's dedicated time (40 hrs/week for full-time), equipment and workstation, HR management, time tracking, and our managed services layer — which includes onboarding support, performance reviews, communication facilitation, and admin overhead. There are no hidden costs. Rate differences between seniority levels reflect experience depth in Machine Learning specifically, not just years in the industry. Rate differences also reflect certification depth — AWS Certified Machine Learning Specialty and Google Professional Machine Learning Engineer certified developers may be priced at the higher end.

Yes. Our Machine Learning developers hold certifications including AWS Certified Machine Learning Specialty, Google Professional Machine Learning Engineer, TensorFlow Developer Certificate. While ML certifications demonstrate foundational knowledge, we weight production experience more heavily — training models in courses is different from deploying and monitoring them in production.