What You’ll Do

📍Define and execute the SRE and DevOps strategy for AI platforms, ensuring high availability, scalability, and security. 📍Architect and oversee cloud-native infrastructure across AWS, GCP, and Azure for AI workloads. 📍Drive Kubernetes-based orchestration for AI models, including GPU scheduling and resource optimization. 📍Establish CI/CD pipelines for AI platform and AI model lifecycle management (training, testing, deployment) with enterprise-grade security and compliance. 📍Implement observability frameworks and reliability standards (SLIs, SLOs, SLAs) for distributed AI systems. 📍Lead incident management, root cause analysis, and performance optimization across compute, storage, and network layers. 📍Collaborate cross-functionally to translate business and functional requirements into resilient technical designs. 📍Stay ahead of trends in SRE, DevOps, MLOps, and AI infrastructure to drive innovation and operational excellence.

What We’re Looking For

📍Education: B.S./M.S. in Computer Science, Engineering, or related field. 📍Experience: 8+ years in platform engineering, SRE, DevOps or AIOps roles. 📍Technical Expertise: 📍Proficiency in Python, Bash/Shell for automation, orchestration, and AI workflows. 📍Proficiency in Rust build and dependency management frameworks. 📍Hands-on expertise with CI/CD tools (e.g., Azure DevOps, Jenkins, GitHub Actions etc.). 📍Proven ability to design and scale fault-tolerant, cloud-native systems for AI workloads. 📍Deep proficiency in Kubernetes (Helm, Kustomize, CRDs) and containerization (Docker, containerd).

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