We are seeking a highly skilled DevOps / Platform Engineer to join our R&D team. In this role, you will be responsible for designing, operating, and scaling our cloud infrastructure while supporting both engineering and machine learning initiatives.
You will own our AWS environment end-to-end - from infrastructure architecture and Kubernetes operations to MLOps enablement, security hardening, and cost optimization. This role goes beyond traditional DevOps and includes responsibilities across IT operations support, multi-account AWS management, DevSecOps practices, DevFinOps ownership, and machine learning infrastructure.
You will play a critical role in ensuring our systems are secure, scalable, observable, and cost-efficient, while enabling fast and reliable software and model delivery.
Responsibilities
Cloud Infrastructure & Platform Ownership
Design, implement, and maintain scalable infrastructure in a multi-account AWS organization
Manage and deploy applications using Helm charts, GitOps workflows using ArgoCD
Support CI/CD pipelines and release processes.
Observability & Reliability
Implement and maintain monitoring and logging solutions.
Define alerts, dashboards, and SLOs to ensure system health and operational excellence.
MLOps Enablement
Support and operate ML infrastructure in AWS
Enable reliable model deployment, monitoring, and lifecycle management.
DevSecOps & Security
Implement security best practices across infrastructure and CI/CD pipelines
Enforce IAM least-privilege policies and secure networking configurations
Integrate security ownership and compliance controls.
DevFinOps & Cost Optimization
Monitor and optimize AWS resource utilization and costs
Implement cost visibility and reporting practices
IT & Operational Support
Support internal IT-related infrastructure tasks
Continuously improve operational processes and automation
Requirements: ~5 years of experience working in a DevOps or Platform Engineering role.
BSc degree in Computer Science, Engineering, or equivalent (required).
Strong hands-on experience operating in an AWS environment.
Solid understanding of networking fundamentals, including: VPC design, subnets, routing tables, Load balancers, NAT/Internet gateways.
Experience maintaining Kubernetes clusters at scale, including managing and deploying Helm charts.
Strong background in observability and monitoring using: Prometheus, Grafana, and Loki and CloudWatch.
Experience with GitOps workflows and continuous delivery using ArgoCD.
Proficiency with Infrastructure as Code (Terraform / Cloudformation), preferably using AWS CDK (Python and/or TypeScript).
Core understanding of MLOps principles, including model deployment, monitoring, versioning, and lifecycle management.
This position is open to all candidates.