Vetted MLOps engineers
Hire MLOps engineers who get models into production and keep them healthy
MLOps engineers build the pipelines, serving, and monitoring that move machine learning models from a data scientist's notebook into reliable production. Through Grape5, you hire India-based MLOps engineers, pre-vetted on real pipeline and infrastructure work, dedicated to your product, with at least 4 hours of daily US overlap and a typical start in 2 to 3 weeks.
In short
MLOps engineers build the pipelines, serving, and monitoring that move machine learning models from a data scientist's notebook into reliable production.
Through Grape5, you hire India-based MLOps engineers, pre-vetted on real pipeline and infrastructure work, dedicated to your product, with at least 4 hours of daily US overlap and a typical start in 2 to 3 weeks.
When to hire MLOps engineers
- Your data scientists keep handing off models as notebooks, and nobody owns getting them served, versioned, and retrained on a schedule in production.
- A model that worked at launch is quietly degrading, and you have no drift or data-quality monitoring to catch it before customers complain.
- You are moving from batch scoring to real-time inference and need low-latency serving on Kubernetes or a managed platform without runaway GPU bills.
- You are standing up an LLM or RAG feature and need someone to own the vector store, retrieval pipeline, evals, and inference cost, not just prompt tweaks.
How we vet MLOps engineers
Every engineer we put forward is screened by a senior Grape5 engineer before you meet them. For MLOps engineers, we look specifically at:
- Whether they can design a reproducible training-to-serving pipeline in an orchestrator like Airflow, Kubeflow, Dagster, or Metaflow, including how they handle backfills, retries, and idempotency.
- How they version and promote models with a registry such as MLflow or Weights & Biases, and how they roll back a bad model with canary or shadow deploys instead of downtime.
- Whether they can instrument monitoring for data drift, concept drift, and training-serving skew, and set alert thresholds that catch real regressions without paging on noise.
- Their handling of feature stores and offline/online consistency (Feast, Tecton), and whether they can spot point-in-time correctness and label-leakage failure modes.
- GPU and cost decisions: request batching, autoscaling, spot versus on-demand, and serving with vLLM, Triton, or quantization so inference does not blow the budget.
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 builds the model; an MLOps engineer builds the system that trains, serves, monitors, and retrains it reliably. There is overlap, and a strong ML engineer may cover both. Tell us which end of that spectrum you need, and we vet for it specifically instead of assuming one title fits.
They work inside your accounts and access controls, the same as any remote hire. Most teams scope least-privilege IAM roles, use a VPN or bastion, and keep secrets in their own vault. We do not dictate your security model; the engineer follows the access and compliance rules you set.
Honest answer, it depends on how many models you run and how often they change. One model that retrains monthly is very different from a dozen with real-time inference. Grape5 engineers are dedicated to your product for the engagement, so you get consistent ownership rather than someone context-switching across clients.
You get at least 4 hours of daily overlap with US hours for standups, reviews, and handoffs. For incidents, teams usually agree on an on-call and alerting setup up front. The offset can help: a drift or latency alert overnight in the US can be triaged while your team sleeps.
If the fit is wrong, Grape5 replaces them free. Because we pre-vet on real pipeline, serving, and monitoring work rather than resume keywords, mismatches are rare, but MLOps stacks vary a lot. Tell us your orchestrator, cloud, and serving setup up front so we match against it.
Tell us the role. Get vetted profiles.
Send us the seniority and stack you need. We’ll come back with a shortlist of vetted MLOps engineers who’ve shipped it, and a plan to start in 2 to 3 weeks.