Hire MLOps Engineers
Hire vetted MLOps Engineers through Hevcode: fully remote, starting in 48 hours, with timezone-overlap working hours and a risk-free trial. 534+ projects shipped over 6 years.
Get skilled MLOps engineers to deploy, monitor, and automate ML in production. CI/CD for models, feature stores, and reproducible pipelines. Start within 48 hours.
Prefer email? Reach me at contact@hevcode.com.
534+ projects delivered | 273+ verified reviews | Start in 48 hours
Last updated: June 2026
Looking to hire MLOps engineers to get your models out of notebooks and into reliable production? Our engineers build the infrastructure that turns a one-off model into a system you can retrain, deploy, monitor, and roll back with confidence.
Most ML projects stall at the same place: a model that works on a laptop but has no deployment path, no versioning, no monitoring, and no way to reproduce yesterday's result. Our engineers close that gap with CI/CD for ML, feature stores, experiment tracking, and drift detection so your models keep working after launch.
Whether you need someone to stand up MLflow or Kubeflow, automate your training pipelines, or set up model monitoring and rollback, we offer flexible engagement models matched to your ML maturity and cloud stack.
Technical Skills
Our developers are proficient in these technologies and more
Model Deployment
- Model serving (TorchServe, Triton, BentoML)
- Containerization (Docker)
- Kubernetes and KServe
- REST and gRPC inference APIs
- Batch and real-time inference
- GPU scheduling and autoscaling
CI/CD for ML
- ML pipeline orchestration (Kubeflow, Airflow)
- MLflow experiment tracking and registry
- DVC and data versioning
- Automated retraining pipelines
- GitHub Actions and GitLab CI
- Reproducible environments
Feature Stores and Data
- Feast and feature stores
- Feature engineering pipelines
- Online and offline serving
- Data validation (Great Expectations)
- Streaming features (Kafka)
- Schema and lineage tracking
Monitoring and Cloud
- Drift and performance monitoring (Evidently, Prometheus)
- Model rollback and shadow deployment
- AWS SageMaker, GCP Vertex AI, Azure ML
- Infrastructure as code (Terraform)
- Cost and resource optimization
- Logging, tracing, and alerting
Why Hire Through Us
Benefits of hiring developers through Hevcode
Production-First Engineers
Our MLOps engineers care about reproducibility, monitoring, and rollback, so models keep working long after the demo.
Pre-Vetted Experts
Every MLOps engineer passes technical assessments and has shipped real ML systems with CI/CD, registries, and monitoring in place.
Quick Onboarding
Start working with your MLOps engineer within 48 hours. No long search for scarce infrastructure talent.
Direct Communication
Work directly with the engineer building your pipelines. No layers between you and your ML infrastructure.
Timezone Overlap
We ensure 4+ hours of overlap with your timezone for live deployments and incident response.
Risk-Free Trial
Start with a 1-week risk-free trial. If the fit is not right, you pay nothing.
Engagement Models
Flexible hiring options to match your needs
Dedicated Engineer
A full-time MLOps engineer working exclusively on your ML platform, from deployment pipelines to monitoring and feature infrastructure.
Ideal for: Companies scaling ML across multiple models, ongoing platform work
Development Team
A complete team with MLOps engineers, a data engineer, and a cloud architect to build your end-to-end ML platform.
Ideal for: Enterprises standardizing ML delivery, multi-model production systems
Hourly/Part-Time
Flexible hours to set up a registry, automate one pipeline, add monitoring, or audit your ML infrastructure. Pay only for time worked.
Ideal for: Pipeline automation, monitoring setup, infra audits, consulting
Hiring Process
Simple 4-step process to get your developer
Share Requirements
Tell us about your models, your cloud stack, how you train and deploy today, and the gaps you feel. We scope the right MLOps foundation for your maturity.
Developer Matching
Within 24 hours, we present 2-3 pre-vetted MLOps engineers matched to your cloud and tooling, with relevant platform work and availability.
Interview and Select
Interview the candidates, review their deployment and monitoring approach, and select the engineer who fits your stack and team.
Start Building
Your engineer joins within 48 hours. We set up cloud and repo access, environments, and kick off the platform work.
Frequently Asked Questions
Common questions about hiring developers
What experience level do your MLOps engineers have?
Our MLOps engineers have 4-8+ years across ML and infrastructure, with strong DevOps fundamentals. They are fluent in Kubernetes, MLflow or Kubeflow, model serving, and cloud ML platforms like SageMaker, Vertex AI, and Azure ML.
How quickly can an MLOps engineer start on my project?
We can have an engineer onboarded within 48 hours of selection. For urgent work like a broken deployment pipeline or production model issue, we can often start within 24 hours.
What if the engineer is not the right fit?
We offer a 1-week risk-free trial. If you are not satisfied with the work or fit, we replace the engineer at no cost or provide a full refund. After the trial, replacements are available with 1-week notice.
Do your engineers work in my timezone?
We ensure a minimum 4-hour overlap with your working hours, which is important for live deployments and incident response. Many of our engineers adjust schedules to maximize overlap with US and EU hours.
How do you ensure quality in MLOps work?
Our engineers build for reproducibility and observability: versioned data and models, automated pipelines, infrastructure as code, drift monitoring, and tested rollback paths. We deliver infrastructure your team can operate and trust, not a one-off script.
Can I hire a full team to build an ML platform?
Yes. We provide complete teams with MLOps engineers, data engineers, and cloud architects for end-to-end platform delivery. Teams scale from 2 to 8+ members based on the number of models and your cloud footprint.
Ready to Hire MLOps Engineers?
Get matched with expert MLOps engineers in 24 hours. Get your models reliably into production starting in 48 hours.
Or email contact@hevcode.com.