Description
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.

Responsibilities
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.
Own reference architectures for:
GenAI applications (RAG, fine-tuning, prompt engineering)
Agentic AI systems (multi-agent orchestration, agent-to-agent communication)
AI platform services (model serving, vector search, AI gateways).
Guide long-term AI platform roadmaps aligned to business strategy and cloud architecture principles.
AI / GenAI / Agentic AI Solution Design




Architect end-to-end AI solutions including:
Data ingestion and feature pipelines
Model training, evaluation, and deployment
Retrieval-Augmented Generation (RAG) using vector databases
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

📌 Sr It Architect Bengaluru
🏢 Honeywell
📍 Bengaluru