Machine Learning Observability Platform Engineer
1 hour ago
We’re looking for a Machine Learning Observability Platform Engineer who’s passionate about building large-scale, reliable ML systems. You’ll help design and enhance our open-source observability platform, adding AI capabilities that power critical insights across enterprise environments.
What You’ll Do- Build and maintain AI/ML features for an open-source Observability Platform built on Grafana and ClickHouse.
- Collaborate with SREs, service owners, and observability SMEs to ensure scalable, reliable ML model deployment.
- Design and manage data pipelines using Databricks and related tools.
- Use CI/CD and MLOps best practices to automate model deployment and testing.
- Deploy and manage ML infrastructure on AWS or Azure.
- Set up and integrate MCP servers and connect tools across observability systems.
- Establish prompt standards and develop custom MCP integrations between systems.
- Troubleshoot ML system performance and reliability using OpenTelemetry pipelines and observability metrics.
- Master’s degree in Computer Science, Engineering, or Artificial Intelligence (or equivalent experience).
- Proven experience designing, developing, and operating ML systems in production.
- Hands-on experience with LLMs/MCPs, Grafana, and Databricks.
- Strong coding skills in Python.
- Familiarity with Kubernetes, container orchestration, and cloud platforms (AWS/Azure).
- Solid understanding of observability pillars (metrics, logs, traces).
- Experience implementing OpenTelemetry pipelines for ML systems.
- Knowledge of CI/CD, MLOps, and monitoring best practices.
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