Vetted, dedicated ML engineers
Hire ML engineers who ship models to production, not just notebooks
An ML engineer builds, trains, and ships machine learning models into production, and keeps them working: data pipelines, feature engineering, model serving, and monitoring for drift. Grape5 connects US companies with India-based ML engineers, pre-vetted on live code and system design, dedicated to your product, and backed by a free replacement if the fit is wrong.
In short
An ML engineer builds, trains, and ships machine learning models into production, and keeps them working: data pipelines, feature engineering, model serving, and monitoring for drift.
Grape5 connects US companies with India-based ML engineers, pre-vetted on live code and system design, dedicated to your product, and backed by a free replacement if the fit is wrong.
When to hire ML engineers
- You have a data scientist's prototype sitting in a notebook and need someone to turn it into a versioned, monitored service with a real inference API.
- A recommendation or ranking model has quietly degraded in production and you need someone to diagnose data drift and rebuild the retraining pipeline.
- You are adding a RAG or LLM feature and need someone to own embeddings, the vector store, evaluation, and the cost and latency tradeoffs.
- Your inference bill keeps climbing and you need someone to quantize, batch, or right-size GPU serving without hurting model accuracy.
How we vet ML engineers
Every engineer we put forward is screened by a senior Grape5 engineer before you meet them. For ML engineers, we look specifically at:
- We check that they prevent training/serving skew and data leakage: can they explain how a feature computed at training differs from serving, and describe a leak that once inflated their offline metrics.
- We probe evaluation depth: choosing metrics that match the business (precision and recall tradeoffs, calibration, ranking metrics) instead of raw accuracy, and building offline and online evaluation that actually predicts production behavior.
- We test serving and deployment: packaging a model behind a low-latency API (TorchServe, Triton, BentoML, or a managed equivalent) with batching, versioning, and rollback.
- We look at pipeline and reproducibility work: reproducible training runs, experiment tracking with MLflow or Weights & Biases, and monitoring for drift and degradation after launch.
Grape5 vs a freelancer marketplace
Grape5
- Who the engineer works for
- Vetted, dedicated, and backed by Grape5 for your engagement.
- Vetting
- Screened by our own senior engineers, code, system design and communication, before you ever meet them.
- Timezone
- 4+ hours of daily overlap with your US working hours, in your tools and standups.
- If it isn't working
- We replace them from the bench, usually within days, at no extra cost.
- Continuity
- The same team, retained and growing with your product.
A freelancer marketplace
- Who the engineer works for
- An independent contractor juggling several clients at once.
- Vetting
- Self-reported skills, a résumé and a star rating.
- Timezone
- Whatever hours the contractor decides to keep.
- If it isn't working
- You re-post the role and start the search from scratch.
- Continuity
- Churn between contracts, the context leaves when they do.
| Grape5 | A freelancer marketplace | |
|---|---|---|
| Who the engineer works for | Vetted, dedicated, and backed by Grape5 for your engagement. | An independent contractor juggling several clients at once. |
| Vetting | Screened by our own senior engineers, code, system design and communication, before you ever meet them. | Self-reported skills, a résumé and a star rating. |
| Timezone | 4+ hours of daily overlap with your US working hours, in your tools and standups. | Whatever hours the contractor decides to keep. |
| If it isn't working | We replace them from the bench, usually within days, at no extra cost. | You re-post the role and start the search from scratch. |
| Continuity | The same team, retained and growing with your product. | Churn between contracts, the context leaves when they do. |
Related roles you can hire
Pre-vetted engineers across adjacent skills, dedicated to your product and your US working hours.
Frequently asked questions
A data scientist explores data and builds prototype models; an ML engineer productionizes them: pipelines, serving, monitoring, and retraining. If your model already works in a notebook but nothing ships or stays healthy in production, you likely need an ML engineer. Grape5 vets for the production craft, not just modeling.
That depends on your controls, and you set them. Engineers can work inside your cloud, your access boundaries, and your data-handling rules. Discuss residency, PII handling, and audit needs up front so the engagement is scoped to your requirements. We do not make legal or compliance guarantees on your behalf.
Training and serving infrastructure (GPUs, cloud accounts, data warehouses) lives on your side, since the models and data are yours, and the engineer works in your environment. Grape5 provides the engineer, dedicated to your product; you provide the stack and access. Infrastructure cost is separate and not something we mark up.
Senior Grape5 engineers run live sessions, not take-home theater. We probe for leakage, training/serving skew, honest evaluation, and real serving and monitoring experience, the things that separate a notebook demo from a model that holds up. If the fit is still wrong once they start, the replacement is free.
A typical start is 2 to 3 weeks, because we match for your stack and problem rather than filling a seat. The engineer is dedicated to your product for the engagement and managed and backed by Grape5, so if the fit is wrong we replace them free. You are not on your own the way you would be with a freelancer or marketplace hire.
Tell us the role. Get vetted profiles.
Send us the seniority and stack you need. We’ll come back with a shortlist of vetted ML engineers who’ve shipped it, and a plan to start in 2 to 3 weeks.