VP, AI/ML & Agentic Engineering
Tasks
- Build RAG systems with vector databases
- Build and scale AI/ML platforms
- Define prompt engineering and safety standards
- Deploy and operate LLMs for fine tuning and inference
- Design CI/CD pipelines for ML
- Develop agentic and multi agent frameworks
- Drive MVP delivery and experimentation
- Implement model governance and validation
- Implement observability for models and systems
- Lead MLOps pipeline development
- Manage low latency and high throughput systems
- Manage model risk management including kill switches and rollback
- Operate real time ML inference services
- Own AI ML platform architecture
- Partner with InfoSec Legal and Compliance
Perks/Benefits
- Career tracks and job levels
- Employee assistance program
- Employee benefits
- Free financial counseling
- Health coaching
- Hybrid work arrangement
- Leadership development
- PTO
- Parental leave
- Tuition assistance program
- Virtual training opportunities
Skills/Tech-stack
A/B | A/B Testing | AIML Platform Architecture | AWS | Agentic AI | Auditability | Automated Deployment | Azure | B testing | Batch Processing | CI/CD | Cloud Computing | Containers | Databricks | Experiment tracking | GCP | Generative AI | Infrastructure as Code | Kubernetes | Language Models | Large Language Models | Low Latency | Low Latency Inference | MLOps | Machine Learning | Model Governance | Model Monitoring | Model Registry | Model Risk Management | Model risk | Model versioning | Observability | Platform Architecture | Prompt engineering | Real Time | Real-time Systems | Retrieval-Augmented Generation | Risk Management | Security Governance | Snowflake | Streaming Machine Learning | Time Systems | Vector Databases | “as-code”
Education
Regions
Countries
States
Cities
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