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Vi is an enterprise AI platform for healthcare, serving Fortune 500 health providers, pharma, and consumer brands with over 195 million daily users across its Activate, Engage, Operate, and Pulse product lines. The company is backed by $150M in funding and operates from offices in New York, Austin, Boston, and Tel Aviv.
We are seeking a Senior DevOps/Platform Engineer to join our R&D team and own our cloud infrastructure end-to-end. This role combines traditional DevOps responsibilities with MLOps enablement, DevSecOps, and DevFinOps ownership. You will design, operate, and scale our AWS environment to support both engineering and machine learning initiatives, ensuring systems are secure, scalable, observable, and cost-efficient.
Key Responsibilities:
- Design, implement, and maintain scalable multi-account AWS infrastructure
- Manage application deployments using Helm charts and GitOps workflows with ArgoCD
- Support CI/CD pipelines and release processes
- Implement and maintain monitoring, logging, and observability solutions (Prometheus, Grafana, Loki, CloudWatch)
- Define alerts, dashboards, and SLOs for operational excellence
- Support and operate ML infrastructure in AWS, enabling reliable model deployment and lifecycle management
- Implement DevSecOps best practices, enforce IAM least-privilege policies, and secure networking
- Monitor and optimize AWS resource utilization and costs
- Support internal IT infrastructure tasks and continuously improve operational processes
Required Qualifications:
- ~5 years of DevOps or Platform Engineering experience
- BSc in Computer Science, Engineering, or equivalent
- Strong hands-on AWS experience
- Solid networking fundamentals (VPC, subnets, routing, load balancers, NAT/Internet gateways)
- Experience managing Kubernetes clusters at scale with Helm charts
- Strong observability background (Prometheus, Grafana, Loki, CloudWatch)
- GitOps and ArgoCD experience
- Infrastructure as Code proficiency (Terraform, CloudFormation, AWS CDK with Python/TypeScript)
- Core MLOps understanding (model deployment, monitoring, versioning, lifecycle management)
