AI Solution Architect

Location

Hyderabad (Hybrid )

Core Responsibilities:

• Lead Architecture Design: Lead the technical planning and architecture of AI/ML solutions using Microsofts AI ecosystem (Azure cloud services, Microsoft 365 Copilot, Agent 365, etc.), ensuring designs are scalable, secure, and aligned with business needs . Translate complex business challenges into architecture blueprints that leverage autonomous AI agents and advanced ML models for tangible business value .
• AI Solutions Implementation: Oversee end-to-end implementation of AI solutions – from proof-of-concept to production deployment. This includes hands-on leadership in building and configuring AI components, managing development sprints, and ensuring quality delivery. Ensure that the solutions are enterprise-grade, with proper scalability, resiliency, and performance in production.
• Integrate OpenAI Technologies: Integrate and deploy OpenAI models (e.g., GPT-4, Codex) via OpenAI API or Azure OpenAI Service into enterprise applications to enable intelligent features (such as natural language understanding, code generation, and predictive analytics). Leverage these generative models to build capabilities like chatbots, copilots, and automation within products, all while maintaining enterprise-grade security and compliance .

• AI Agents & Copilots Development: Develop and orchestrate AI agents and Copilot solutions using Microsofts frameworks (e.g., Copilot Studio, Semantic Kernel) and Agent 365. Ensure these agents are properly configured, trained, and governed to meet design specifications and deliver tangible business value . This includes using Agent 365 as the control plane to deploy, organize, and securely manage AI agents across our platforms .

Required Qualifications & Skills

• Experience: 10+ years of relevant experience in software development and solution architecture, including a proven track record in architecting and deploying AI/ML solutions in production. Experience should demonstrate increasing responsibility, with leadership in end-to-end solution delivery for enterprise projects.
• AI/ML Architecture Expertise: Deep knowledge of AI/ML architecture and design patterns, including experience with enterprise AI systems (such as NLP, predictive analytics, recommender systems or generative AI). Must have hands-on experience translating business requirements into AI technical solutions and integrating machine learning models into enterprise applications.
• Microsoft AI Ecosystem: Extensive experience with Microsofts AI technologies – including Azure AI services (Azure Machine Learning, Cognitive Services, Azure OpenAI, etc.) and the Microsoft 365 AI ecosystem. Hands-on expertise with Microsoft Copilot (e.g. customizing Microsoft 365 Copilot or GitHub Copilot) and related tools, as well as Semantic Kernel for building AI plugins, is required . Experience with Agent 365 or similar AI agent frameworks for deploying and managing AI agents is a strong requirement.
• OpenAI Integration: Proven experience integrating OpenAI (or Azure OpenAI) models into solutions. Familiarity with OpenAIs API and SDKs, and practical knowledge of using models like GPT-3.5/4 or Codex in real-world applications. Ability to fine-tune or customize pre-trained models for domain-specific tasks is a plus.

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  Cloud & Software Development Skills: Strong background in cloud architecture and software engineering. Proficiency in programming (e.g., Python, C#/.NET or Java) for AI solution development and integration. Solid understanding of API development, microservices, and containerization (Docker/Kubernetes) in the context of deploying AI services. Experience with DevOps/MLOps practices for CI/CD, model deployment, and monitoring in Azure or similar cloud environments is expected .
• Leadership & Communication: Excellent leadership, communication, and collaboration skills. Ability to work across teams and articulate complex AI concepts to both technical teams and business stakeholders. Experience leading technical teams or mentoring engineers is highly valued. Proven ability to collaborate in a cross-functional environment (with product managers, data scientists, etc.) to drive project success is required .

Preferred Qualifications

• Education & Certifications: Masters in Computer Science, Data Science, or a related field is a plus. Relevant Microsoft certifications (e.g., Azure Solutions Architect, Azure AI Engineer, or Data Scientist certifications) will be an advantage, indicating validated expertise in Microsofts cloud and AI technologies.
• Power Platform Experience: Familiarity with Microsoft Power Platform (Power BI, Power Apps, Power Automate) and its integration with AI solutions is a plus . For example, experience using Power Virtual Agents or AI Builder to extend solution capabilities would be beneficial.