Hire Elite MLOps Engineers in 10–14 Days
Vetted, Reliable, Scalable
Faster than hiring
Safer than outsourcing
Built for healthtech
Looking to boost your ML infrastructure without compromising on speed, cost, or model quality?
At CareMinds, we help you scale AI pipelines, automate model delivery, and manage ML lifecycle with top-tier MLOps talent, validated by our in-house tech leads.
From request to ready-to-go MLOps engineer in just 10–14 days.
Why Choose CareMinds for MLOps Staff Augmentation?
Here’s why businesses around the world trust us to augment their MLOps teams:
Tech-Lead Vetted Talent Only
- Candidates are screened and approved by our tech lead
- We validate cloud-native skills, pipeline automation, ML lifecycle fluency, and collaboration with data teams
Onboard in 10–14 Days
- From brief to start in under two weeks
- You skip endless screening and go straight to top-tier, pre-qualified candidates
Deep MLOps Domain Knowledge
Our engineers are fluent in real-world tooling:
- Kubeflow, MLflow, Airflow, SageMaker, TFX
- Docker, Kubernetes, Terraform
- CI/CD for ML models
They understand versioning, testing, reproducibility, and monitoring in ML workflows
Global Talent with Timezone Fit
- Engineers from Eastern Europe, LATAM
- Flexible overlap with your core team, no communication bottlenecks
Risk-Free Engagement
- No long-term lock-ins
- Easy scalability up or down
- Guaranteed replacement if the match isn’t right
Ready to Launch Your ML Projects Faster?
What MLOps Developers from CareMinds Bring to the Table
Our MLOps engineers are fluent in both machine learning and software infrastructure, enabling them to help your team with:
Building and maintaining CI/CD pipelines for ML models
Engineers proficient in automating CI/CD workflows for machine learning models, ensuring smooth, secure, and efficient code deployment across all environments.
Creating and managing Kubernetes-based ML platforms
Engineers with expertise in building scalable, Kubernetes-based platforms for deploying and managing machine learning models, ensuring flexibility and high availability.
Automating model training, validation, and deployment
Engineers skilled in automating the entire ML pipeline, from data preprocessing and model training to validation and deployment, optimizing time and reducing manual intervention.
Orchestrating ML pipelines using Apache Airflow, Kubeflow, or Prefect
Engineers proficient in orchestrating complex ML workflows with tools like Apache Airflow, Kubeflow, and Prefect, ensuring reliable, efficient, and automated model execution.
Monitoring models in production with tools like Prometheus, Grafana, Seldon Core
Engineers skilled in monitoring ML models in production environments using tools like Prometheus, Grafana, and Seldon Core, ensuring high performance, alerting, and continuous improvement.
Optimizing cost and compute with cloud-native deployments (AWS/GCP/Azure)
Engineers with expertise in leveraging cloud-native services (AWS, GCP, Azure) to optimize resource usage and minimize costs, ensuring scalable and efficient deployments of ML models.
Ensuring reproducibility, explainability, and compliance across the lifecycle
Engineers focused on maintaining model reproducibility, transparency, and compliance, using advanced tools and frameworks to meet industry standards and regulatory requirements like HIPAA.
Get MLOps engineers who deliver — not just code, but outcomes!
Engagement Models That Fit
Whether you’re building an ML product from scratch or scaling production workloads, we’ll fit your team structure:
Dedicated MLOps developers — full-time team members
Milestone/project-based — short-term or flexible staffing
Hybrid squads — mix of MLOps + data scientists + backend/infra engineers
Team extension — plug into your existing MLOps or data science team
Industries We Serve
Fintech: Fraud detection models, KYC automation
Healthcare: HIPAA-compliant ML ops, diagnostic models
Telecom & IoT: Real-time signal processing pipelines
Retail & E-commerce: Personalization engines, recommender systems
Manufacturing: Predictive maintenance, anomaly detection
Why CareMinds Is Your Best MLOps Partner
We don’t just match keywords on resumes — we deliver pre-qualified, infrastructure-ready MLOps engineers that understand data, code, models, and production environments.
Our process:
Requirement Alignment
Requirements deep-dive
Pre-screening by AI & infra tech leads
Live candidate vetting by Tech Lead
Business context fit — soft skills, communication, and project awareness
Final shortlist in under 2 weeks
No guesswork. No wasted interviews. Just ready-to-hire experts.
Our Clients Say
Bring Your ML to Production — with the Right Engineers
FAQ
Do your MLOps developers support both cloud and on-prem environments?
Yes, our engineers are experienced across AWS, GCP, Azure, and hybrid setups.
Can I scale my MLOps team after the initial hire?
Absolutely. We offer scalable staff augmentation for fast-growing ML teams.
How do you ensure data security and compliance?
All engineers follow your NDAs, access policies, and industry-specific compliance guidelines.
