- Define and document global AI platform patterns (Discovery Zone, BYOD, end to end ML lifecycle, reference architectures) and obtain approvals through architecture forums.
- Design Model Serving Architecture
- Partner with the AI Tech Lead on the Model Serving PoC and define the end to end architecture for scalable and secure model deployment.
- Define MLOps Architecture
- Define and document MLOps architecture using learnings from the Model Serving PoC and drive approvals for standardization. Distribute models through Kubernetes
- Enable LLM Strategic Capabilities
- Assess different capabilities for LLM (Azure AI foundry, Bedrock and Databricks) and take design decisions for approval
- Work with security, Data protection office, other architecture teams to align them with the decision and secure approvals for roll out
- Document the low level design for LLM roll out to different use case
Evolve Platform Capabilities
Drive incremental platform capabilities (Responsible AI, GenAI, Data Governance, etc.) aligned with the approved L2 architecture.
📌 Lead AI Platform Architect (India)
🏢 Orcapod Consulting Services
📍 India
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