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Flexible Engagement Models for Every Need
Choose the right model that fits your business needs, timeline, and budget.
Staffenza places MLOps Engineers as ML developers with MLflow, Kubeflow and AWS SageMaker to cut hiring to 7-21 days. Engineers run CI with GitLab. MLOps engineers build pipelines with Docker, Kubernetes and Airflow, monitor drift via Prometheus and Grafana across fintech and healthcare. Teams manage registries with DVC.
1,000+ pre-vetted IT professionals and 7–21 days average time-to-hire. Staffenza supplies MLOps ML Developers who build production model pipelines, CI/CD for models, experiment tracking, drift monitoring, model registry and cost-aware serving using MLflow, Kubeflow, Kubernetes, Terraform, SageMaker across fintech, healthcare, retail, AV and NLP.
Your next MLOps Engineers is already vetted.
MLOps engineers help you build reproducible pipelines, deploy and monitor production models, and manage infrastructure and compliance across industries.
Automate training and deployment pipelines with Airflow, Kubeflow, and GitLab CI. Ensure reproducibility, experiment tracking, versioning, and safe rollouts for production models.
Set up observability with Prometheus and Grafana. Track performance and data drift. Alert on regressions and latency spikes for fraud, NLP, CV, and recommendation models.
Design autoscaling GPU clusters and training with Kubernetes and Docker. Use Seldon and TensorFlow. Apply model quantization and batching to lower latency and cost.
Manage registries with MLflow or DVC. Enforce versioning, access controls, and audit logs for GDPR and industry rules. Run reproducible experiments and generate compliance reports.
Deliver ML across fintech, healthcare, retail, autonomous vehicles, telecom, and advertising. Build feature stores with Feast and run data checks with Great Expectations.
Staffenza places pre-vetted MLOps engineers as ML developers in technology, fintech, healthcare, e-commerce, autonomous vehicles, computer vision, NLP, recommendation systems, fraud detection, manufacturing, telecom, and ad tech. They run model lifecycle, CI/CD, experiment tracking, registries, feature stores, deployment, monitoring, drift detection, and cost control.
You get scalable infrastructure, governance, reproducible pipelines, and faster releases. We use Docker, Kubernetes, MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, Feast, and Weights & Biases. Hire from a vetted shortlist in 7 to 21 days.
Staffenza MLOps engineers build ML pipelines, automate training and deployment, monitor model drift, manage model registry, and optimize inference costs for your apps.
We match 2 to 5 pre-screened MLOps Engineers to your stack within 48 hours. Zero recruiter calls. No commitment required.
Ready to hire a top-tier MLOps Engineers? Tell us the role, experience level, and budget you have in mind. We’ll match you with vetted candidates in 7 to 21 days.
Prefer to talk first? Reach out via email or phone and our team will respond within one business day.
Start with Python and Docker. Candidates should have 3+ years operating Kubernetes, Terraform, MLflow, Kubeflow, Prometheus, Grafana, and CI/CD with GitLab or Jenkins on AWS.
Staffenza delivers candidates in days. Timeline shows you MLOps hires in 7 to 21 days and production within 4 weeks using SageMaker or Vertex AI.
Focus on FinTech and healthcare. Monitoring requirements rise for e-commerce, autonomous vehicles, advertising, telecom, and manufacturing with 24/7 alerts and 4 audits per year.
Request demos and code samples. Review your pipeline builds in Kubeflow or Airflow, check MLflow experiment logs, and assign a 4-hour paid task.
Choose full-time, contract, or managed. Pricing ranges from $30 to $180 per hour, includes temp-to-hire, and shows 85% retention at 12 months.
