VP AI Engineering
USD 115K-140K (estimate) Executive-level Full Time
Tasks
- Apply prompt engineering best practices
- Automate AI workflows
- Build AI engineering organization
- Define AI engineering strategy
- Design autonomous AI systems
- Develop AI platform roadmaps
- Drive AI use case delivery ROI
- Ensure explainability auditability and compliance
- Ensure secure AI integrations
- Establish evaluation and benchmarking
- Establish responsible AI governance
- Govern AI regulatory risk mitigation
- Host and orchestrate AI models
- Implement Retrieval-Augmented Generation (RAG)
- Implement guardrails and structured outputs
- Integrate vector databases
- Lead AI architecture development and deployment
- Lead change management for AI adoption
- Manage AI cost and reliability
- Manage model lifecycle
- Manage vendor relationships
- Mentor engineering leaders
- Monitor performance and drift
- Orchestrate multi-agent workflows
- Oversee AI platform enterprise architecture decisions
- Oversee AI production operations
- Perform deterministic validation
- Present AI strategy to executives
- Set LLM integration standards
Perks/Benefits
Skills/Tech-stack
AI Governance | Agent Orchestration | Artificial Intelligence | Auditability | Cloud Native | Cloud Native Architecture | Deterministic Validation | Distributed Systems | Drift Detection | Enterprise Integration | Evaluation and Benchmarking | Explainability | Guardrails | LLM integration | Language Models | Large Language Models | Lifecycle Management | Machine Learning | Microservices | Model Drift | Model Drift Detection | Model Lifecycle | Model Lifecycle Management | Multi-Agent | Multi-agent orchestration | Observability | Performance Monitoring | Prompt engineering | RAG | Responsible AI | Retrieval-Augmented Generation | Secure integration | Structured Output | Vector Databases | Workflow automation
Education
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