Lead ML Ops/DevOps Engineer – AI Engineering

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, construct, and sustain scalable and resilient data and machine learning (ML) pipelines, infrastructure, and workflows utilizing tools such as Terraform, GitHub Actions, ArgoCD, Helm, among others.

• Automate the provisioning of infrastructure and management of configurations using cloud-native services, preferably on AWS, through tools like Terraform and CloudFormation.

• Create, containerize, and oversee Kubernetes (EKS) clusters and/or ECS environments within AWS.

• Collaborate with development teams to enhance performance, deployment strategies, and cost-efficiency.

• Partner with DevOps and Site Reliability Engineering (SRE) teams to guarantee 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 the establishment of CI/CD pipelines for model training, validation, and deployment.

• Implement observability for data pipelines and ML services utilizing tools such as Prometheus, Grafana, Datadog, or similar solutions.

• Develop and sustain automated pipelines for model retraining, monitoring drift, and version control in a production environment.

• Support experimentation and prototyping in fields like Machine Learning and Generative AI, transitioning successful prototypes into production systems.

• Ensure that cloud infrastructure remains secure, compliant, and cost-effective by adhering to best practices in governance, identity, and access management.

⛳️ Requirements

• Over 8 years of experience in DataOps, MLOps, or related domains, with at least 3 years dedicated to ML model operationalization and workflow automation.

• Expertise in AWS services, including EC2, S3, IAM, ACM, Route 53, CloudWatch, EKS, and ECS.

• Familiarity with infrastructure as code (IaC) tools such as Terraform, CloudFormation, and Helm.

• Knowledge of CI/CD practices for ML pipelines, GitOps methodologies, and tools such as GitHub Actions, Jenkins, or Argo Workflows.

• Strong scripting and automation capabilities using Python or GitHub workflows.

• Comprehensive understanding of observability and monitoring tools (e.g., Prometheus, Grafana, Datadog, or OpenTelemetry).

• A solid grasp of security best practices for cloud and Kubernetes environments, including secrets management, identity and access control, and policy enforcement.

• A strong understanding of data governance, lineage, and metadata management is advantageous.

• Exceptional collaboration and communication abilities, with a proven track record of effective work in cross-functional, globally distributed teams.

• A bachelor's degree in computer science, a related field, or equivalent practical industry experience.

🏝️ Benefits

• An inclusive culture that strongly embodies our core values: Act Like an Owner, Delight Our Customers, and Earn the Respect of Others.

• The chance to make a meaningful impact and grow professionally by leveraging your unique strengths and engaging in valuable learning experiences.

• Highly competitive compensation, benefits, and rewards programs that encourage you to perform at your best every day and be recognized for your contributions.

• A vibrant, people-first work environment that promotes work/life balance, offers employee resource groups, and organizes social events to foster interaction and camaraderie.