This role leads architecture for cloud-native AI applications leveraging Microsoft Azure, Azure Databricks, LLMOps/MLOps, and enterprise system integrations, ensuring solutions meet standards for security, scalability, governance, and business value.
The architect partners closely with AI engineers, data teams, platform teams, product owners, and business stakeholders to translate complex business problems into referenceable, reusable AI architectures.
Key Responsibilities
Enterprise AI Architecture & Strategy
Define and maintain enterprise-wide architecture standards for AI/ML, GenAI, and Agentic AI platforms across Azure and Databricks.
Agent frameworks for task orchestration and enterprise workflows.
Design multi-LLM strategies (Azure OpenAI, open-source, and commercial LLMs) with abstraction layers and fallback patterns.
Define agent registry, agent orchestration, and governance models for enterprise-scale usage.
Azure & Databricks Platform Architecture
Lead architecture for Azure-native AI stacks
Architect Azure Databricks for: ML training and inference, LLM fine-tuning and evaluation, Vector search and embedding pipelines, MLflow-based lifecycle management.
Define cost-optimized, secure, and scalable cloud reference patterns.
Enterprise Integration & Interoperability
Define integration patterns between AI platforms and: ERP, CRM, PLM, HCM systems. APIs, event-driven architectures, and messaging platforms.
Architect AI Gateway and API management patterns for GenAI and agent access.
