Sr. Site Reliability Engineer
Quick Summary
Define, monitor, and maintain Service Level Objectives (SLOs) and Service Level Indicators (SLIs) for critical AI/ML services.
Expert-level knowledge of Kubernetes (K8s) and Docker. MLOps Stack: Familiarity with tools such as Kubeflow, Vertex AI, MLflow, or DVC.
We are seeking a high-caliber Site Reliability Engineer (SRE) to join our Forward Engineering team. You will be the guardian of our production ecosystems, ensuring that our complex, data-driven AI platforms remain resilient, scalable, and highly performant. This role is a hybrid of software engineering and systems architecture, with a specialized focus on MLOps—bridging the gap between model development and production-grade reliability.
Responsibilities
~1 min read- SLA/SLO Management: Define, monitor, and maintain Service Level Objectives (SLOs) and Service Level Indicators (SLIs) for critical AI/ML services.
- Error Budgeting: Manage error budgets to balance the velocity of feature releases from the ML team with the stability of the production environment.
- Scalability: Architect and manage auto-scaling strategies for Kubernetes (GKE) to handle fluctuating workloads during model training and high-volume inference.
- Model Serving Reliability: Ensure the high availability of Vertex AI endpoints and custom inference services.
- GPU/TPU Optimization: Monitor and optimize compute resource utilization (accelerators) to ensure cost-efficient performance for Large Language Models (LLMs).
- Pipeline Resilience: Support and stabilize ML pipelines (Vertex AI Pipelines/Kubeflow) to ensure seamless data flow from ingestion to model retraining.
- Infrastructure as Code (IaC): Use Terraform or Pulumi to provision and manage consistent, version-controlled cloud environments.
- CI/CD & GitOps: Design and optimize robust deployment pipelines for both application code and ML models using GitHub Actions, Cloud Build, or ArgoCD.
- Task Automation: Develop custom Python or Go scripts to automate repetitive operational tasks, self-healing mechanisms, and resource cleanup.
- Observability: Build and manage comprehensive dashboards using Prometheus, Grafana, or Google Cloud Operations Suite (Stackdriver).
- Incident Management: Act as a primary responder in on-call rotations, leading the technical resolution of production outages.
- Blameless Post-Mortems: Conduct deep-dive root cause analysis (RCA) to ensure systemic issues are identified and permanently remediated through code.
Requirements
~1 min readWhat We Offer
~1 min readBenefits
Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.
Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.
Location & Eligibility
About this job
- Posted
- 189 days ago
Listing Details
- Posted
- May 8, 2026
- First seen
- September 29, 2026
- Last seen
- November 13, 2026
Posting Health
- Days active
- 44
- Repost count
- 0
- Trust Level
- 31%
- Scored at
- November 13, 2026
Signal breakdown
We provide certainty by solving your toughest challenges.
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