Senior Principal AI Architect/Engineer
Tasks
- Apply infrastructure as code for cloud deployments
- Build CI CD pipelines and deployment rollback automation
- Build governance dashboards for Responsible AI compliance
- Build telemetry pipelines for metrics logs traces and events
- Capture safety signals and guardrail trigger metrics
- Create safety security and red teaming observability
- Define continuous quality engineering metrics and quality gates
- Design AI observability architecture for agents and LLMs
- Develop Python tooling for observability exports and anomaly detection
- Implement observability for agent fleets memory and harness systems
- Instrument OpenTelemetry across agent platforms
- Instrument marketplace and registry usage and health telemetry
- Integrate responsible AI signals into observability pipelines
- Monitor memory skills MCP and reinforcement learning signals
- Partner with stakeholders on observability roadmaps and adoption
Perks/Benefits
- N/A
Skills/Tech-stack
AI Observability | AWS | Alerting | Anomaly Detection | Auditability | Azure | Bias detection | CI/CD | Causal Inference | Cause analysis | Clustering | Cost telemetry | Dashboards | Data Retention | Distributed tracing | Event Streaming | Explainability | Fairness | FinOps | GCP | GPU efficiency | Incident Management | Infrastructure as Code | Kafka | Logging | Metrics | OpenTelemetry | PII Detection | Policy Evaluation | Process control | Python | Red Teaming | Reinforcement Learning | Reinforcement Learning Harness | Responsible AI | Root Cause Analysis | Root cause | SLO/SLA | Security observability | Semantic tagging | Series analysis | Software testing | Statistical Process Control | Telemetry | Threat modeling | Time Series | Time Series Analysis | Token consumption | Zero Trust | “as-code”
Education
N/A
Roles
AI | AI Architect | AI Engineer | Architect | Engineer | Principal | Principal AI Architect | Principal AI Engineer
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