System Engineer-AI Ops

Role Mission

Own deployment, configuration, release, upgrade, infrastructure integration, and operational readiness for the AD data platform and its supporting infrastructure.

Key Responsibilities

• Develop repeatable deployment and release mechanisms for platform applications using Docker, Kubernetes, Helm, deployment scripts, and configuration-management practices.

• Deploy, configure, and maintain shared platform components such as Kafka, PostgreSQL, MongoDB, Redis, Elasticsearch, Airflow, Prefect, Argo Workflows, Kubeflow, Ray, Spark, and Flink.

• Integrate platform applications and distributed workloads with HDFS, OSS, CPU resources, GPU resources, networking, DNS, TLS, authentication, and monitoring services.

• Maintain development, integration, test, staging, and production environments and ensure environment configurations remain reproducible.

• Standardize installation, configuration, upgrade, rollback, and release procedures across platform components.

• Support Alpha, Beta, and GA deployment activities and coordinate production rollout with development teams.

• Develop backup, recovery, disaster-recovery, and rollback procedures for critical platform components.

• Diagnose and resolve infrastructure, deployment, compatibility, performance, and resource-utilization issues.

• Develop and maintain deployment documentation, operational procedures, troubleshooting guides, and runbooks.

• Improve deployment automation, release reliability, infrastructure observability, and operational efficiency.

• Work with application and infrastructure engineers to define resource requirements and deployment architectures for new platform capabilities.

Required Qualifications

• 4+ years of DevOps, platform engineering, SRE, or deployment engineering experience.

• Strong Linux administration skills.

• Strong Docker and Kubernetes experience.

• Experience deploying distributed applications.

• Strong understanding of networking, DNS, TLS, storage, and security fundamentals.

• Experience with CI/CD systems and automated deployments.

• Experience troubleshooting production infrastructure.

Preferred Qualifications

• Experience deploying Spark, Flink, Kafka, Airflow, Prefect, Argo Workflows, Ray, or Kubeflow.

• Experience deploying PostgreSQL, MongoDB, Redis, and Elasticsearch.

• Experience operating HDFS and object-storage-backed systems.

• Experience operating GPU workloads.

• Experience supporting data platforms or ML platforms.

• Experience with compliance-controlled or on-premise cloud environments.