Sr. Site Reliability Engineer
Tiger Analytics Inc.6 months ago
Washington, DC, USASenior
Responsibilities
- Define, monitor, and maintain SLOs and SLIs for critical AI/ML services.
- Manage error budgets and balance ML feature-release velocity with production stability.
- Architect and manage Kubernetes autoscaling strategies for model training and high-volume inference.
- Ensure high availability of Vertex AI endpoints and custom inference services.
- Optimize GPU and TPU utilization for cost-efficient LLM performance.
- Support and stabilize Vertex AI Pipelines and Kubeflow ML pipelines.
- Provision and manage cloud environments with Terraform or Pulumi.
- Design deployment pipelines for application code and ML models using GitHub Actions, Cloud Build, or ArgoCD.
- Develop Python or Go automation, self-healing mechanisms, and resource-cleanup tools.
- Build monitoring and observability dashboards with Prometheus, Grafana, or Google Cloud Operations Suite.
- Participate in on-call rotations, lead technical resolution of production outages, and conduct blameless post-mortems and root-cause analysis.
Requirements
- Expert-level knowledge of Kubernetes and Docker.
- Familiarity with Kubeflow, Vertex AI, MLflow, or DVC.
- Strong proficiency in Python and Bash; knowledge of Go is a plus.
- Experience managing the reliability of data-heavy services such as BigQuery, Pub/Sub, or vector databases including Pinecone and Milvus.
- Solid understanding of VPCs, load balancers, DNS, and secure service meshes such as Istio and Anthos.
Benefits
- Significant career development opportunities in a small, fast-growing, challenging, and entrepreneurial environment.
- High degree of individual responsibility.
- On-site position in Washington, United States.
Tech Stack
Categories
DevOpsSite Reliability
About Tiger Analytics Inc.
Tiger Analytics Inc. provides data and AI consulting services that deliver machine learning, analytics, and cloud data engineering solutions for large enterprises across industries, including life sciences. The company engages on strategy and implementation projects, from modern BI and data platforms to MLOps, helping Fortune 100 clients turn data into business outcomes. Founded in 2011 and headquartered in Santa Clara, it is privately held with operations across North America, Europe, and Asia-Pacific.