AI/ML Deployment and Enablement Leader
Tasks
- Define SLAs KPIs and observability standards
- Define unified AI stack and architectural patterns
- Design scalable modular AI ecosystem
- Develop self service AI platforms APIs and tooling
- Ensure regulatory security privacy compliance
- Establish AI playbooks and best practice frameworks
- Evaluate AI technologies and define adoption roadmaps
- Govern end to end AI lifecycle
- Implement model governance versioning data lineage explainability auditability
- Lead AI Center of Excellence
- Lead MLOps practices including CI CD model registry automated rollback
- Oversee high availability AI model deployment
- Provide feasibility guidance for AI use cases
- Resolve delivery blockers for data readiness platform onboarding governance dependencies
- Set AI standards and direction
Perks/Benefits
- 401k matching
- Adoption Assistance
- Disability insurance
- Healthcare (Medical Dental Vision)
- Life insurance
- Paid Holidays
- Paid vacation
- Parental leave
- Sick and Safe Leave
Skills/Tech-stack
AI Services | API Development | AWS AI | AWS AI Services | Agentic AI | Amazon Bedrock | Apache Airflow | Artificial Intelligence | Auditability | Azure AI | Azure AI Services | CI/CD | Cloud Architecture | Data Ingestion | Data Lineage | Elasticsearch | Explainability | Feature Stores | High Availability | Inference | Langchain | Langgraph | Low Latency | Low-Latency Systems | MLOps | Machine Learning | Model Governance | Model Registry | Monitoring | Observability
Education
Regions
Countries
States
Cities
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