Sr. Dir., AI Engineering
Santa Clara, CALIFORNIA, United States
R
USD 251K-385K Senior-level Full Time
Tasks
- Architect GPU TPU accelerated confidential computing environments
- Build LLMOps MLOps pipelines for model deployment
- Define AI engineering standards reusable platform patterns best practices
- Develop model orchestration fine tuning distillation function calling
- Drive enterprise LLM model strategy
- Embed AI Control Tower discovery observability governance
- Ensure data residency and sovereignty compliance
- Extend governance reliability to non platform AI hyperscaler and third party models
- Influence executives on AI infrastructure investment build versus buy
- Lead ServiceNow Platform AI
- Lead and scale global engineering data science and data engineering organizations
- Own AI ready data pipelines feature and data serving layers data quality
- Own AI security observability identity access controls runtime telemetry
Perks/Benefits
- 401k match
- ESPP
- Family leave programs
- Flexible spending accounts
- Flexible time away
- Health plans
- Matching donations
Skills/Tech-stack
AI Platform | AI Security | API first | API-first architecture | AWS | Access Management | Azure | CCPA | Chaos Engineering | Cloud Native | Confidential Computing | Container Orchestration | Data Residency | Data Sovereignty | Event Driven | Event-driven architecture | Fine Tuning | Function Calling | GDPR | GPU Computing | Google Cloud | High Availability | High Performance | High-Performance Computing | Identity and Access Management | Identity and access | Infrastructure as Code | Kafka | Kubernetes | LLMOps | Language Models | Large Language Models | MLOps | Microservices | Model Distillation | Model Orchestration | Observability | Performance Computing | Runtime Monitoring | SRE | Service Mesh | Spark | Streaming Architecture | TPU Computing | Zero Trust | “as-code”
Education
N/A
Regions
Countries
States
Cities
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