About this opportunity
Tiger Analytics, LLC lists this Sr. Site Reliability Engineer opportunity in washington, District of Columbia. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Role Overview
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.
Key Responsibilities
1. Reliability & Performance Engineering
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.
2. MLOps & AI Infrastructure
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.
3. Automation & Orchestration (Eliminating "Toil")
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.
4. Monitoring, Alerting & Incident Response
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.
Orchestration: Expert-level knowledge of Kubernetes (K8s) and Docker.
MLOps Stack: Familiarity with tools such as Kubeflow, Vertex AI, MLflow, or DVC .
Scripting: Strong proficiency in Python (for automation) and Bash; knowledge of Go is a plus.
Data Systems: Experience managing the reliability of data-heavy services (BigQuery, Pub/Sub, or Vector Databases like Pinecone/Milvus).
Networking: Solid understanding of VPCs, Load Balancers, DNS, and secure service mesh (Istio/Anthos).
Benefits
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.
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Worksite address
washington, DC, 20022, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.