Optimize cloud costs while maintaining performance and reliability standards
Work with ML engineers to develop efficient MLOps pipelines for model training, evaluation, and serving
Establish and maintain security, compliance, and data governance practices for AI systems
Mentor engineering teams on infrastructure best practices and cloud architecture patterns
Drive incident response, postmortems, and continuous improvement of system reliability
Skills and attributes for success
4+ years of infrastructure, DevOps, or cloud platform engineering experience
Deep hands-on expertise with Kubernetes and container orchestration (Docker, Kubernetes)
Expert proficiency in at least one major cloud platform (AWS, GCP, or Azure)
Strong programming background in Python, Go, or similar languages
Deep understanding of distributed systems, networking, and storage concepts
Experience with GPU infrastructure, CUDA, or other accelerated computing environments
Hands-on experience with ML infrastructure tools and frameworks (Kubeflow, MLflow, Ray, or similar)
Strong understanding of infrastructure-as-code tools (Terraform, CloudFormation, Helm)
Experience with CI/CD pipelines and automation frameworks
Passion for monitoring, logging, and observability – experience with Prometheus, ELK, Datadog, or similar
Experience with database and storage systems optimization for large-scale workloads
Preferred education and experience
Bachelors/Masters in Computer Science or a related field with 4-7 years of professional experience.
