AI/MLOps SRE Lead Engineer
Tasks
- Architect ChatOps solutions for operational events
- Assess architectural risks and recommend improvements
- Automate platform operations
- Build CI/CD pipelines
- Deliver secure scalable production AI ML solutions
- Design build and scale ML platform infrastructure
- Design self-healing systems
- Develop observability with anomaly detection and predictive analytics
- Drive service reliability availability and performance
- Establish SLOs SLIs error budgets
- Evaluate AI native operational technologies
- Implement automated remediation
- Implement infrastructure-as-code
- Integrate observability and automated remediation
- Lead LLM SLM RAG and AI Agent platform monitoring
- Mentor engineers and advise reliability engineering MLOps and cloud strategy
- Perform technical debt assessments
Perks/Benefits
Skills/Tech-stack
AKS | AWS CDK | Amazon SageMaker | Anomaly Detection | Automated remediation | Bash | CI/CD | ChatOps | Cloud Cost Optimization | Cloud Governance | Compliance Automation | Container Orchestration | Cost Optimization | Databricks | Datadog | Dataiku | Distributed tracing | EKS | Experiment tracking | Feast | GKE | Go | Google Vertex | Google Vertex AI | Grafana | Infrastructure as Code | Kubeflow | Kubernetes | LLM | Logging | MLflow | Metrics | Model Deployment | Model Evaluation | Model Monitoring | Model Training | Multi-cloud | Multi-cloud Governance | OpenTelemetry | Pipeline Orchestration | Policy-as-Code | Predictive Analytics | Prometheus | Pulumi | Python | RAG | SLM | Terraform | Vertex AI | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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