Principal GenAI Architect

Job Description :

We are looking for an experienced Principal GenAI Architect to define the architecture, technology strategy, and implementation standards for enterprise Generative AI and Agentic AI solutions. The role will lead the design and delivery of production-grade AI platforms and applications using Microsoft Foundry, Azure OpenAI, RAG, multi-agent architectures, MCP, A2A, and Azure cloud services.

The ideal candidate should have deep hands-on engineering experience combined with strong enterprise architecture, technical leadership, AI governance, and solution-design expertise. The individual will work across architecture, engineering, product, data, security, and business teams to drive enterprise adoption of scalable and responsible AI solutions.

Key Responsibilities :

- Define the enterprise architecture and technical strategy for Generative AI and Agentic AI solutions.

- Lead architecture and implementation of AI solutions using Microsoft Foundry, Azure OpenAI, and Azure AI services.

- Evaluate and select suitable LLMs and supporting technologies using the Microsoft Foundry model catalog, including OpenAI, Llama, Mistral, MAI, and other relevant models.

- Architect and govern enterprise RAG solutions using Azure OpenAI Embeddings, Azure AI Search, Foundry IQ, and related retrieval technologies.

- Define architecture for AI agents and multi-agent systems using Microsoft Agent Framework, Foundry Agent Service, and equivalent technologies.

- Design agentic capabilities including tool calling, orchestration, memory, context management, state management, exception handling, and multi-agent coordination.

- Lead implementation of MCP-based tool integrations and A2A communication patterns.

- Define and govern AI guardrails, Content Safety, responsible AI controls, security policies, and governance frameworks.

- Establish AI evaluation standards covering quality, accuracy, relevance, reliability, safety, latency, and cost.

- Define and oversee MLOps/LLMOps, CI/CD, model lifecycle, observability, and production-readiness practices.

- Lead model optimization and fine-tuning initiatives and determine the appropriate use of RAG, prompting, fine-tuning, model routing, and model selection.

- Define reusable AI platform services, APIs, deployment templates, reference architectures, and engineering standards.

- Ensure AI solutions are designed for scalability, resilience, security, availability, maintainability, and cost efficiency.

- Lead architecture reviews, technical design workshops, technology assessments, and architecture decision-making.

- Partner with senior business and technology stakeholders to understand complex requirements and translate them into scalable AI solutions.

- Lead PoCs and innovation initiatives to evaluate emerging GenAI technologies and determine their enterprise applicability.

- Provide technical leadership and mentorship to AI, ML, software, and platform engineering teams.

- Review designs and implementations to ensure alignment with enterprise architecture and engineering standards.

- Support complex production issues and drive architectural improvements and root-cause resolution.

- Contribute to AI transformation roadmaps, technology strategy, and long-term platform evolution.

- Stay current with advancements in LLMs, Agentic AI, AI platforms, model orchestration, multi-agent protocols, and responsible AI.

Required Skills & Experience :

- 14 - 19 years of overall experience in software engineering, AI/ML engineering, solution architecture, or enterprise architecture.

- 3+ years of hands-on experience in Generative AI and Agentic AI.

- Deep hands-on experience with Microsoft Foundry and/or Azure OpenAI in production environments.

- Strong expertise in RAG, embeddings, vector search, Azure AI Search, and enterprise retrieval architectures.

- Strong experience with AI agent architecture, orchestration, tool calling, memory, state management, and multi-agent systems.

- Practical experience with the Microsoft Agent Framework and/or Foundry Agent Service.

- Hands-on experience with MCP and A2A or equivalent multi-agent integration patterns.

- Strong understanding of AI evaluation, benchmarking, guardrails, Content Safety, and responsible AI.

- Experience with Azure Machine Learning, MLflow, MLOps/LLMOps, CI/CD, and AI observability.

- Strong programming expertise in Python and/or .NET.

- Strong Azure cloud knowledge, including services such as Blob Storage, Entra ID, Container Apps, Azure AI Search, and related platform services.

- Strong understanding of cloud-native architecture, distributed systems, microservices, APIs, and enterprise integration.

- Proven experience defining reference architectures, technology standards, and enterprise AI strategies.

- Experience leading architecture and technical design across multiple teams and complex enterprise environments.

- Strong stakeholder management skills, including engagement with senior leadership, architects, engineering teams, and business stakeholders.

- Excellent communication, presentation, technical storytelling, and decision-making skills.

- Ability to take AI initiatives from strategy and PoC through enterprise implementation and production adoption.

Skills

Generative AI, Agentic AI, RAG, Python, Azure, LLMOps, AI Architecture, LLM, Artificial Intelligence, Machine Learning