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🤖 Data, AI & ML Engineers

Hire Dedicated MLOps Engineers — India, US, UK & Australia

Hire pre-vetted MLOps engineers who automate the path from trained model to reliable, monitored production service. Golonex places dedicated India-based MLOps engineers with US, UK, Australian, and Indian teams — working in your timezone, under NDA, onboarded within one week and with no lock-in.

The role

What a MLOps Engineer does

An MLOps engineer builds the infrastructure and automation that gets ML models to production and keeps them healthy. Day to day they design training and deployment pipelines, set up CI/CD for models, manage model registries and feature stores, containerise and serve models, and implement monitoring for drift, performance, and cost — plus automated retraining and rollback.

Core expertise

MLflow & model registry Kubeflow / pipeline orchestration Docker & Kubernetes CI/CD for ML (GitHub Actions, Jenkins) Model serving (KServe, Triton, Seldon) Feature stores (Feast) Cloud ML (SageMaker, Vertex AI, Azure ML) Monitoring & drift detection Terraform & infrastructure as code Experiment tracking Data & model versioning (DVC) Automated retraining pipelines GPU scheduling & optimisation Observability (Prometheus, Grafana)
Outcomes

What a Golonex MLOps Engineer builds for you

Automated training-to-deployment ML pipelines
CI/CD workflows for model testing and release
Model registries, feature stores, and versioning
Scalable model-serving infrastructure on Kubernetes
Drift, performance, and cost monitoring with alerting
Automated retraining and safe rollback strategies
Engagement

How it works

Engagement models

Full-time dedicated Part-time / hourly Remote Hybrid Onsite (US · UK · India)

Based in India, working in your timezone. Onsite options available across the US, UK, and India.

Category

🤖

Data, AI & ML Engineers

Demand: High demand
Why Golonex for MLOps Engineers

The Golonex difference

Named, pre-vetted engineers

Every MLOps 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.

FAQ

Hiring a MLOps Engineer — FAQs

How quickly can I hire an MLOps engineer through Golonex? +

Typically within one week. We shortlist pre-vetted MLOps engineers matched to your stack — Kubeflow, MLflow, SageMaker, or Vertex AI — you interview them, and the selected engineer is onboarded to your environment within 5 business days, in your timezone.

Can your MLOps engineers productionise models our data scientists built? +

Yes. They take experimental models and wrap them in reproducible pipelines, containerised serving, CI/CD, and monitoring — closing the gap between a working notebook and a reliable production service. All infrastructure code and IP is yours.

Do they set up monitoring and automated retraining? +

They do. Our MLOps engineers instrument models for drift, latency, and performance, wire alerting through Prometheus and Grafana, and build automated retraining and rollback so degradation is caught and corrected without manual firefighting.

Will the engineer work in my timezone? +

Yes. Although based in India, our MLOps engineers work overlapping hours with US, UK, and Australian teams. On-call and coverage windows are agreed before the engagement so deployments and incident response align with your schedule.

How do you vet MLOps engineers? +

Every candidate passes a technical assessment on real scenarios — pipeline automation, model serving, and monitoring design — plus a communication and remote-working assessment and reference checks from at least two prior engagements before you meet them.

Get started

Ready to hire a MLOps Engineer?

Tell us the role, stack, and timeline. We'll match you with a named, pre-vetted engineer — integrated within 1 week.