Build and maintain infrastructure to support real-time and batch ML workloads
Implement observability tools (logging, monitoring, alerting) for model performance and system uptime
Design and manage CI/CD pipelines for ML and data applications
Ensure high availability, disaster recovery, and rollback capabilities for production environments
Manage access controls, secrets, and security policies in collaboration with compliance and IT
Troubleshoot incidents, lead postmortems, and drive root-cause resolution
Work with U.S. and international teams to provide 24/7 coverage across time zones

3–6 years of experience in DevOps, SRE, or backend engineering roles
Proficient with tools like Docker, Kubernetes, Terraform, GitLab/GitHub Actions, Airflow
Strong scripting in Python or Bash and familiarity with Linux environments
Experience deploying and monitoring ML models or data pipelines in production
Knowledge of observability stacks (e.g., Prometheus, Grafana, ELK, Datadog)
Familiarity with cloud platforms (e.g., AWS, GCP, or Azure)
Strong documentation, problem-solving, and incident response skills
Preferred Qualifications:
Experience supporting ML/AI workflows using Palantir Foundry.
Exposure to compliance frameworks like SOC 2, ISO 27001, or financial regulations
Knowledge of MLOps frameworks (e.g., MLflow, Kubeflow, SageMaker Pipelines)
Ability to automate deployments, testing, and monitoring at scale

Site Reliability Engineer

Key skills