Azure AI Solution Architect

@koolvishruth

Hi, I am certified in Databricks.I am from Bangalore.I will build proof of concept as mentioned.I am AI Engineer with 8 years of experience. Lets connect

@bitnetservices

I can deliver this POC by building a structured, governed agent architecture directly on Azure AI Foundry integrated with Databricks and Purview. The implementation would start with defining a certified KPI layer tied to Unity Catalog Gold tables. Instead of letting the LLM “interpret” metrics, I would configure domain-specific agents that execute approved SQL templates mapped to validated business definitions. An orchestrator agent (using Semantic Kernel or AutoGen) would handle intent classification and route requests to Financial, Clinical, or other scoped agents. For consistency, I would introduce a semantic KPI registry (JSON/YAML-based) where synonyms map to the same SQL logic. That ensures different phrasings resolve to the same governed query and return identical results. On governance, the assistant would run under Managed Identity with Azure RBAC and Unity Catalog permissions enforced. I have hands-on experience implementing PHI controls, Purview tagging, column-level masking, and audit traceability in regulated healthcare environments. The agent would behave as a normal governed identity — no privilege bypass. I’ve built Azure-based data platforms using Databricks Lakehouse architecture, Unity Catalog, ADLS, and SQL-based RAG patterns. I’ve also worked in healthcare-regulated environments where PHI handling, lineage, and explainability were critical. Happy to discuss scope, timeline and rate further.

@anirudhat

Building a governed "Source of Truth" AI assistant in a HIPAA-regulated healthcare environment requires deep expertise across Azure AI Foundry, Databricks Unity Catalog, and Microsoft Purview—all of which I've worked with extensively in production settings. I'll architect a multi-agent system using Semantic Kernel, with an Orchestrator agent routing natural language queries to domain-specific sub-agents (Financial, Clinical, Marketing) that resolve varying phrasings to your verified Gold Layer tables through a semantic mapping layer, ensuring deterministic, consistent KPI results. Each response will include full citation lineage back to the source Databricks table. Governance will be enforced via Purview-driven PHI masking and Azure RBAC applied at the agent identity level. I'm available to start immediately.

@gopalkatariya44

I have more than 3+ year of experience in the cloud with AIML, I am the best fit for your poc or I can scale as needed.

@vivek2014094

I have experience building AI solutions on Azure, including agent-based workflows and governed data retrieval systems. For this POC, I can design an orchestrator agent that routes user queries to domain-specific agents (Financial, Clinical, etc.) using Semantic Kernel or similar tools. To ensure consistent “Source of Truth” responses, I will implement a SQL-based RAG approach over Databricks Unity Catalog Gold tables, so different phrasings map to the same validated dataset. I can also demonstrate PHI masking and RBAC using Purview and Azure identity controls. The demo will include citation-based answers (linking back to Databricks tables), a simple deployment setup, and a clear architecture diagram. I’m comfortable working in regulated environments and explaining technical decisions clearly.

@josephm011293

Hi, This is a well-scoped project — I've built similar multi-agent RAG systems on Azure that query governed data layers and return cited, consistent answers. Here's my relevant experience: Agentic AI on Azure — Built orchestrator-style architectures where a routing agent delegates to domain-specific sub-agents. Experienced with Semantic Kernel for multi-agent workflows and tool orchestration. SQL-based RAG & Data Consistency — Implemented systems that map natural language queries to verified Gold-layer tables, ensuring identical results regardless of phrasing. This "Source of Truth" pattern is central to my RAG work. Databricks & Unity Catalog — Hands-on with Unity Catalog for data governance, Gold Layer table design, and ADLS integration. Comfortable writing optimized SQL against lakehouse architectures. Governance & HIPAA — Understand PHI security requirements including masking, RBAC, and audit logging. Experience implementing role-based access where the AI agent operates as a governed user identity via Purview and Azure RBAC. Citations & Transparency — Every RAG system I build returns source citations linking to the exact table, column, and query used — not just the answer. Deliverables I'll provide: functional live demo, deployment scripts, and a clear technical architecture diagram your leads can review. Available to start this week. Happy to walk through my approach on a quick call. — Joseph

@nageswarAI

Hi, With over nine years of experience in designing and deploying advanced Machine Learning and Generative AI solutions across finance, legal, healthcare, and automotive sectors, I bring deep expertise in LLMs, RAG architectures, and multimodal AI pipelines. My work focuses on building scalable, production-grade systems that deliver measurable business impact. I excel at leading end-to-end AI initiatives spanning data strategy, model development, deployment, and optimization and thrive in roles that bridge research innovation with practical engineering. Sincerely, Nageswar