3 months ago
Bengaluru, IndiaSenior
Responsibilities
- Design and implement scalable cloud infrastructure for deploying video AI models and services.
- Build and maintain Kubernetes clusters and containerized deployment pipelines optimized for GPU workloads.
- Develop monitoring, logging, and observability systems for AI/ML production services.
- Create infrastructure-as-code practices and deployment automation.
- Optimize cloud costs while maintaining performance and reliability.
- Collaborate with ML engineers on MLOps pipelines for model training, evaluation, and serving.
- Establish security, compliance, and data governance practices for AI systems.
- Mentor engineering teams on infrastructure best practices and cloud architecture.
- Lead incident response, postmortems, and continuous reliability improvement.
Requirements
- 4+ years of infrastructure, DevOps, or cloud platform engineering experience.
- Deep hands-on expertise with Kubernetes and container orchestration, including Docker.
- Expert proficiency with at least one major cloud platform: AWS, GCP, or Azure.
- Strong programming experience 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.
- Experience with ML infrastructure tools such as Kubeflow, MLflow, or Ray.
- Strong understanding of infrastructure-as-code tools including Terraform, CloudFormation, and Helm.
- Experience with CI/CD pipelines and automation frameworks.
- Experience with monitoring, logging, and observability tools such as Prometheus, ELK, or Datadog.
- Experience optimizing databases and storage systems for large-scale workloads.
- Bachelor's or master's degree in computer science or a related field, with 4–7 years of professional experience.
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