Design, implement, and manage highly available infrastructure for our cloud-based platforms (AWS).
Work with our internal engineering teams to architect and support AI/ML infrastructure, specifically managing AWS Lambda infrastructure, and some legacy SageMaker environments for model training, hosting, and inference.
Create and automate robust deployment pipelines using CI/CD tools (GitLab / GitHub Actions) for both web applications and machine learning models.
Build, maintain, and scale containerized applications with Docker and ECS/Fargate.
Implement MLOps best practices to streamline the transition of models from development to production.
Ensure system scalability and reliability through proactive monitoring, logging, and automated alerting.
Collaborate with both Product Engineers and Data Scientists to optimize performance, security, and infrastructure costs.
Manage and evolve our Infrastructure as Code (IaC) footprint.

Requirements

5+ years of experience in a DevOps or infrastructure role.
Expert knowledge of cloud platforms such as AWS, GCP and Azure
Strong experience with containerization technologies (Docker, ECS / Kubernetes).
Proven track record of designing and managing complex CI/CD pipelines.
Experience with MLOps workflows (model versioning, retraining pipelines, or feature stores).
Hands-on experience with monitoring and logging tools (Datadog, Prometheus, Grafana, MLflow).
Expertise in scripting languages (Python is a must, along with Bash, Go, etc.).
Proficiency with infrastructure automation tools (Terraform, Ansible, or CloudFormation).
Excellent communication skills and the ability to bridge the gap between traditional DevOps and Data Science teams.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

AWSGCPAzureDockerECSKubernetesCI/CDMLOpsPythonTerraform

communication