Work Location: Hyderabad (Twice a Week Work from Office)

Experience Range: 10–15 years

Role Summary

We are seeking a hands-on Senior Gen AI Solutions Architect to design, build, and scale enterprise-grade AI applications using Azure OpenAI and modern cloud-native patterns. This role blends solution architecture with deep engineering expertise, focusing on RAG systems, AI agents, and enterprise integrations. You will drive technical strategy while mentoring engineering teams and delivering production-ready Gen AI solutions.

Key Responsibilities

Architect and implement end-to-end Gen AI solutions (RAG systems, AI agents) using Azure OpenAI and Azure services.
Design scalable microservices and orchestration pipelines for LLM workflows using Python and cloud-native principles.
Lead integration of AI systems with enterprise platforms such as SharePoint, Dynamics, Dataverse, and Microsoft Graph APIs.
Define and enforce software architecture standards using SOLID principles and proven design patterns.
Guide engineering teams (Leads, Senior Developers, Junior Developers) on best practices in AI development and cloud engineering.
Drive prompt engineering strategies and optimization for GPT-based and similar models in production scenarios.
Oversee deployment, monitoring, and lifecycle management of AI applications on Azure cloud platforms.

Must-Have Skills (all validated at ≥ 85% confidence)

Azure OpenAI + Azure AI Services (RAG & AI Agents) — Core platform capability required to build enterprise Gen AI solutions.
.NET + Azure Cloud Application Development — Strong foundation to integrate and extend enterprise AI within Microsoft ecosystems.
Python for AI Microservices & LLM Workflows — Essential for building orchestration layers, pipelines, and scalable AI services.
Enterprise Integration (Graph API, SharePoint, Dynamics, Dataverse) — Critical for embedding AI into real business systems.
Design Patterns & SOLID Principles — Required for architecting scalable, maintainable systems at senior level.
Prompt Engineering & Agentic AI Design — Key capability for building effective, production-grade LLM solutions.

Good-to-Have Skills

Vector Databases (e.g., Azure Cognitive Search, Pinecone, etc.) — Enhances RAG performance and search capabilities.
AI Solution Deployment & MLOps Practices on Azure — Supports production reliability and lifecycle management.
Architecture & Design Tools (Lucidchart, Visio, Draw.io) — Useful for communication of system designs and stakeholder alignment.
Thought Leadership in Gen AI Use Cases — Enables strategic contribution and innovation within the organization.