Sr. Enterprise Data & AI Architect
Tasks
- Advise build vs buy and cost models
- Advise product and engineering roadmap
- Architect solutions using Snowflake Cortex
- Create battle cards and field guidance
- Define MLOps and LLMOps patterns
- Define RAG patterns
- Define agentic patterns
- Design AI/ML architectures on Snowflake
- Develop AI investment strategies using ROI and TCO models
- Develop competitive cloud data and AI playbooks
- Develop proof of concept and modernization programs
- Drive knowledge sharing and continuous learning
- Ensure secure governed AI ML deployments
- Guide migration to cloud native AI ready architectures
- Lead cloud integration strategy
- Lead executive customer architecture reviews
- Mentor and develop enterprise architecture practice
- Represent company at industry events
- Serve as trusted advisor for data and AI transformation
- Shape enterprise data and AI architectures
Perks/Benefits
- N/A
Skills/Tech-stack
AI architecture | AI/ML Systems | AWS | Agentic Frameworks | Azure | Cloud Integration | Cloud platform | Compliance | Data Governance | Data Pipelines | Data Privacy | Enterprise Architecture | Google Cloud | Google Cloud Platform | LLM | LLMOps | ML Systems | MLOps | Machine Learning | Production AI | Production AI ML Systems | Python | RAG | Retrieval-Augmented Generation | SQL | Security | Snowflake | Snowflake Cortex | Vector Databases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Architect | Architect | Data Architect | Enterprise Data Architect
Regions
Countries
States
Cities
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