Hire Dedicated Machine Learning Engineers — India, US, UK & Australia
Hire pre-vetted machine learning engineers who take models from notebook to reliable production service. Golonex places dedicated India-based ML engineers with US, UK, Australian, and Indian teams — working in your timezone, under NDA, onboarded within one week and with no lock-in.
What a Machine Learning Engineer does
A machine learning engineer builds, trains, and deploys models that power real product features. Day to day they engineer features and data pipelines, train and tune models with PyTorch, TensorFlow, and scikit-learn, run experiments and evaluation, package models for serving, and put monitoring, versioning, and retraining in place so performance holds in production.
Core expertise
What a Golonex Machine Learning Engineer builds for you
How it works
Engagement models
Based in India, working in your timezone. Onsite options available across the US, UK, and India.
Category
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Data, AI & ML Engineers
The Golonex difference
Named, pre-vetted engineers
Every Machine Learning Engineer is screened for technical depth, communication, and reliability before you meet them — a sub-30% pass rate at the technical stage.
Integrated within 1 week
From enquiry to working team member in 5 business days. We handle onboarding, NDA, and access logistics.
Your timezone
India-based talent working overlapping hours with US, UK, and Australian teams. Daily standups and real-time collaboration, agreed upfront.
NDA-protected, no lock-in
Full IP and confidentiality protection as standard. Month-to-month engagements — scale up or down as your project evolves.
Hiring a Machine Learning Engineer — FAQs
How fast can I onboard a machine learning engineer through Golonex? +
Usually within one week. We shortlist pre-vetted ML engineers matched to your problem — vision, NLP, or tabular modelling — you interview them, and the selected engineer is onboarded to your stack and tooling within 5 business days, in your timezone.
Can your ML engineers take a model all the way to production? +
Yes. Beyond training and evaluation, they package models for serving, add monitoring and drift detection, and set up versioning and retraining — so the model stays accurate in production rather than degrading silently.
Who owns the models and code the engineer produces? +
You do. All trained models, feature pipelines, and code created in the engagement are your intellectual property, delivered under NDA. We retain no rights to your data, models, or outputs.
Do they work with cloud ML platforms? +
They do. Our ML engineers deploy on SageMaker, Vertex AI, and Azure ML, and containerise with Docker and Kubernetes for portable serving. Tell us your cloud and we match engineers with the right platform experience.
How do you vet machine learning engineers? +
Every candidate passes a technical assessment on real scenarios — feature engineering, model training and evaluation, and deployment — plus a communication and remote-working assessment and reference checks from at least two prior engagements before you meet them.
Ready to hire a Machine Learning Engineer?
Tell us the role, stack, and timeline. We'll match you with a named, pre-vetted engineer — integrated within 1 week.