Senior ML Engineer

What you'll do

• Build and maintain scalable ML platform infrastructure using AWS, SageMaker, Kubernetes, and GPU orchestration.
• Design and implement automated infrastructure using Infrastructure as Code tools like Terraform.
• Develop tooling and pipelines to simplify the deployment and productionization of models for Data Scientists.
• Manage secure cloud and on-premise environments for AI/ML, GenAI, and LLM workloads.
• Modernize legacy ML pipelines to improve reliability, scalability, and operational efficiency.
• Establish platform design standards, best practices, and architectural decisions for the ML team.
• Integrate AI/ML solutions into production systems by collaborating with cross-functional engineering teams.

What we're looking for

• A Bachelor's degree and 2+ years of relevant work experience, or 5+ years of relevant work experience.
• Experience designing, building, and maintaining scalable ML platform infrastructure for AI/ML applications (preferred).
• Experience with AWS services including EC2, S3, EKS, SageMaker, IAM, VPC, and CloudWatch (preferred).
• Experience managing Kubernetes clusters and containerized ML workloads using Docker (preferred).
• Experience with ML pipeline and orchestration tools such as Kubeflow, Airflow, or MLflow (preferred).
• Experience building infrastructure automation using Terraform, CloudFormation, or other Infrastructure as Code tools (preferred).
• Experience with Python and shell scripting for automation, tooling, and platform operations (preferred).
• Experience with GPU orchestration, LLMOps, and serving frameworks like vLLM or Triton (preferred).