Design, build, and maintain scalable, resilient data and ML pipelines, infrastructure, and workflows using tools such as GitHub Actions, ArgoCD, Crossplane, Terraform, Helm, and others.
Automate infrastructure provisioning and configuration management using cloud-native services (preferably AWS) with tools like Terraform, CloudFormation, or Crossplane.
Design, containerize, and manage Kubernetes (EKS) clusters and/or ECS environments in AWS.
Collaborate with development teams to optimize performance, deployment, and cost.
Partner with DevOps and SRE teams to ensure high availability, observability, scalability, and security of the data and ML infrastructure.
Work closely with Data Scientists and ML Engineers to operationalize machine learning models, including building CI/CD pipelines for model training, validation, and deployment.
Implement observability for data pipelines and ML services using tools like Prometheus, Grafana, Datadog, or similar.
Develop and maintain automated pipelines for model retraining, monitoring drift, and versioning in production.
Support experimentation and prototyping in areas such as Machine Learning and Generative AI, transitioning successful prototypes into production systems.
Ensure cloud infrastructure is secure, compliant, and cost-efficient, following best practices in governance, identity, and access management.

Requirements

7+ years of experience in DataOps, MLOps, or related fields, with at least 2 years focused on ML model operationalization and workflow automation.
Proficient in AWS services including EC2, S3, IAM, ACM, Route 53, CloudWatch, EKS, and ECS.
Experience with infrastructure as code (IaC) tools such as Terraform, CloudFormation, and Helm.
Familiarity with CI/CD for ML pipelines, GitOps practices, and tools like GitHub Actions, Jenkins, or Argo Workflows.
Strong scripting and automation skills using Python, or GitHub workflows.
Understanding of observability and monitoring tools (e.g., Prometheus, Grafana, Datadog, or OpenTelemetry).
Solid understanding of security best practices for cloud and Kubernetes environments, including secrets management, identity & access control, and policy enforcement.
Familiarity with data governance, lineage, and metadata management is a plus.
Excellent collaboration and communication skills, with a proven ability to work effectively in cross-functional, globally distributed teams.
A bachelor’s degree in computer sciences, Engineering, or a related discipline, or equivalent hands-on industry experience.

Benefits

An inclusive culture strongly reflecting our core values: Act Like an Owner, Delight Our Customers and Earn the Respect of Others.
The opportunity to make an impact and develop professionally by leveraging your unique strengths and participating in valuable learning experiences.
Highly competitive compensation, benefits and rewards programs that encourage you to bring your best every day and be recognized for doing so.
An engaging, people-first work environment offering work/life balance, employee resource groups, and social events to promote interaction and camaraderie.

Applicant Tracking System Keywords

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

Hard Skills & Tools

DataOpsMLOpsML model operationalizationworkflow automationinfrastructure as codescriptingautomationobservabilitymonitoringdata governance

Soft Skills

collaborationcommunicationcross-functional teamwork

Certifications

Bachelor's degree in computer scienceBachelor's degree in Engineering