AI/ML Deployment and Enablement Leader
Tasks
- Define MLOps practices with CI CD and model registry
- Define SLAs KPIs and observability standards
- Define unified AI stack and architectural patterns
- Deliver enterprise AI feasibility guidance
- Develop self service platforms APIs and tooling
- Enable and scale AI platforms and services
- Ensure reliability latency and compliance
- Establish AI playbooks and best practices
- Establish model governance versioning lineage explainability auditability
- Evaluate AI technologies and define adoption roadmaps
- Govern AI lifecycle end to end
- Implement automated rollback
- Lead AI Center of Excellence
- Oversee AI model production deployment
- Own on prem AI platform stack operating model
- Partner to balance performance cost scalability and compliance
- Set AI architecture for modular ecosystem
- Set AI standards and execution mechanisms
Perks/Benefits
- 401k
- Adoption Assistance
- Healthcare (Medical Dental Vision)
- Life insurance
- Long-term disability
- Paid Holidays
- Paid vacation
- Parental leave
- Short-term disability
- Sick and Safe Leave
Skills/Tech-stack
AI architecture | AWS | Agentic AI | Airflow | Amazon Bedrock | Auditability | Azure AI | CI/CD | Data Ingestion | Data Lineage | Elasticsearch | Explainability | Feature Store | Inference | KPIs | Langchain | Langgraph | MLOps | Machine Learning | Microsoft Azure | Model Governance | Model Registry | Observability | Privacy | Regulatory Compliance | SLAs | Security | Training Infrastructure
Education
Regions
Countries
States
Cities
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