7 days 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.
- Establish monitoring, logging, and observability systems for AI/ML production services.
- Develop infrastructure-as-code practices and deployment automation.
- Optimize cloud costs while maintaining performance and reliability.
- Partner with ML engineers on MLOps pipelines for model training, evaluation, and serving.
- Maintain security, compliance, and data governance practices for AI systems.
- Mentor engineering teams on infrastructure and cloud architecture best practices.
- Lead incident response, postmortems, and continuous reliability improvements.
Requirements
- 4+ years of infrastructure, DevOps, or cloud platform engineering experience.
- 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 with 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.
- 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.
Benefits
- Full-time, onsite role in Bangalore.
- Equal opportunity workplace with a stated commitment to diversity and nondiscrimination.
Tech Stack
Categories
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