AI Operations Tech Leader
Tasks
- Apply MLOps AIOps best practices for production ops
- Automate level 1 triage
- Build and mentor AIOps squad
- Build data and AI solutions for incident enrichment
- Collaborate with incident managers service owners and reliability engineers
- Control operational cost at scale
- Create automated post mortems and incident summaries
- Create knowledge mining for support agents
- Design intelligent runbooks
- Develop ticket clustering and automated triage
- Ensure SLAs for reliability low latency auditability and explainability
- Identify and prioritize AI use cases to reduce MTTR and MTTD
- Implement predictive alerting and automated root cause analysis
- Implement rollback mechanisms for production systems
- Integrate AIOps with ITSM and observability tools
- Lead architecture reviews PoCs and vendor evaluations
- Lead operations support AIOps platform design and development
- Monitor model drift and performance degradation
- Perform technical coaching and design code reviews
Perks/Benefits
- N/A
Skills/Tech-stack
ADF | AIOps | Airflow | Anomaly Detection | Automated triage | Automation | Azure | BigPanda | Change Management | Databricks | Datadog | Drift Detection | Dynatrace | ELK | Event Correlation | Explainability | GenAI | Grafana | ITIL | ITSM | Incident Management | JavaScript | Kafka | Knowledge Management | LLM | MLOps | MTTD | MTTR | Machine Learning | Model Drift | Model Drift Detection | Model Monitoring | Moogsoft | Observability | PagerDuty | Predictive Analytics | Problem Management | Prometheus | PyTorch | Python | Service Request | ServiceNow | Spark | Splunk | TensorFlow
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AIOps Engineer | Data Engineer | Engineer | Lead | Learning Engineer | Machine Learning Engineer | Tech Lead
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