Hire DevOps/MLOps Engineers
Add DevOps/MLOps capacity to keep your infrastructure and AI systems reliable, secure, and scalable in production.
Bring in DevOps/MLOps engineers who keep your infrastructure reliable and your ML models running smoothly in production — from CI/CD pipelines to model monitoring and retraining.
What Does a DevOps/MLOps Engineer Do?
A DevOps/MLOps engineer builds and maintains the infrastructure, automation, and pipelines that let software and machine learning models ship reliably and run at scale. MLOps specifically extends DevOps practices to the ML lifecycle: model versioning, automated retraining, drift monitoring, and safe rollback when a model's performance degrades in production.
Core Responsibilities
CI/CD Pipeline Design
Automate build, test, and deployment workflows so releases ship faster and safer.
Cloud Infrastructure & IaC
Provision and manage scalable, cost-efficient infrastructure using Terraform and cloud-native tools.
Model Deployment & Serving
Package and deploy ML models as reliable, low-latency, scalable services.
Monitoring & Observability
Set up logging, alerting, and drift detection to catch issues in infrastructure and models before users do.
Security & Cost Optimization
Harden infrastructure and continuously right-size cloud spend without sacrificing reliability.
Need a DevOps/MLOps engineer to keep production running reliably?
Get a Free Quote →From Requirement to Onboarded Engineer
Requirement
Tell us your infrastructure, cloud provider, and the deployment challenges you're facing.
Shortlist
We present pre-vetted DevOps/MLOps engineers matched to your cloud stack within days.
Interview
You interview and select the engineer that fits your infrastructure and team.
Onboard
The engineer gets access to your infrastructure and starts on priority pipelines.
Scale
Add more engineers or pair with AI/ML and data engineering roles as you grow.
What an Augmented DevOps/MLOps Engineer Delivers
| Project Type | What They Deliver |
|---|---|
| ML model in production | A monitored, auto-scaling model-serving pipeline with rollback safety |
| CI/CD modernization | Automated build, test, and deploy pipelines cutting release time |
| Cloud cost concerns | Right-sized infrastructure with ongoing cost monitoring |
| Infrastructure as code migration | Version-controlled, repeatable infrastructure provisioning |
Hiring Directly vs. Staff Augmentation
Hiring Directly
- Long search for engineers with real MLOps production experience
- Hard to verify infrastructure and reliability track record
- Full salary, benefits, and equipment overhead
- Difficult to scale down if priorities shift
Avtrix Staff Augmentation
- Pre-vetted shortlist matched to your cloud stack within days
- Verified production infrastructure and MLOps experience
- Pay only for the engagement you need
- Scale up or down as your infrastructure needs change
Hiring DevOps/MLOps Engineers FAQs
What seniority levels are available?
We provide mid-level to senior DevOps/MLOps engineers, matched to the complexity of your infrastructure and ML deployment needs.
Can engineers match our cloud provider?
Yes. We shortlist candidates specifically vetted in AWS, GCP, or Azure, matched to your existing environment.
How quickly can an engineer start?
Most requirements receive a shortlist within a few business days, with onboarding shortly after selection.
Do they work remotely or on-site?
Engagements are typically remote with working-hours overlap matched to your team, though on-site arrangements can be discussed.
Can they support both software and ML infrastructure?
Yes. Our DevOps/MLOps engineers cover general application infrastructure as well as ML-specific deployment and monitoring needs.
Is our infrastructure access kept secure and confidential?
Yes. Access is scoped to what's needed for the engagement, and NDAs are available on request.
Ready to Add DevOps/MLOps Capacity?
Tell us your infrastructure and goals, and we'll send a shortlist within days.