Enterprise AI Architect

About the Role

We are looking to add a critical role of Enterprise AI Architect to our dynamic team to help turn AI ideas into secure, scalable, production-ready business solutions. This high-visibility role will define the architecture, patterns, and guardrails that help the company adopt AI responsibly and at scale. The ideal candidate is a hands-on solution architect who can translate business needs into practical AI solutions, design agentic and multi-agent architectures, and partner across business, IT, data, cybersecurity, and operations teams. This is a builder role for someone excited to create the enterprise AI playbook in a global manufacturing and technology environment.

Why This Role Is Exciting

• Help define how enterprise AI is built, governed, and scaled.
• Work on high-value AI use cases that improve real business processes.
• Shape the company’s approach to agents, copilots, AI governance, and responsible adoption.
• Turn experimentation into measurable enterprise impact.

Responsibilities

• AI Strategy & Solution Architecture
  • Define the enterprise AI architecture roadmap, from early use cases to production-ready solutions.
  • Create reusable standards, solution patterns, and best practices for scalable AI delivery.
  • Lead architecture for generative AI, copilots, AI agents, RAG, machine learning, and intelligent workflows.
  • Design agentic and multi-agent solutions with clear controls, escalation paths, and human-in-the-loop checkpoints.
• Azure AI Platform Leadership
  • Architect solutions using Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Microsoft Fabric, Copilot Studio, Power Platform, and related Microsoft AI services.
  • Define when to use copilots, agents, RAG, automation, custom APIs, or third-party AI tools.
  • Evaluate and integrate AI capabilities from outside the Azure ecosystem, including platforms and models from providers such as OpenAI, Anthropic, Google, and others.
  • Design hybrid AI patterns for manufacturing and operational environments that cannot be fully cloud-native.
• Enterprise Data & Systems Integration
  • Ground AI solutions in trusted enterprise data, including ERP, SQL Server applications, and manufacturing/OT systems.
  • Define secure data pipelines, APIs, connectors, and integration patterns using standards such as MCP and A2A where appropriate.
• Cross-Functional Collaboration
  • Partner with business leaders, cybersecurity, infrastructure, data, and development teams to deliver secure, scalable AI solutions.
  • Prioritize AI opportunities based on business value, feasibility, risk, and adoption potential.
• Agile Delivery Leadership
  • Provide technical leadership across Agile delivery teams, including onshore and offshore resources.
  • Guide AI initiatives from concept through production deployment and support.
• AI Governance, Risk & Compliance
  • Establish responsible AI, security, compliance, and governance standards for production AI solutions.
  • Define ALM, LLMOps/MLOps, monitoring, versioning, telemetry, and model evaluation practices.
  • Protect AI models and data workflows through access controls, audit trails, data residency, and prompt-injection safeguards.
• AI Cost Governance (FinOps)
  • Monitor AI compute, API, and cloud costs.
  • Conduct ROI analysis and define success metrics for AI-powered solutions.

Requirements

• 5+ years in solution, cloud, or enterprise architecture.
• 3+ years designing AI, machine learning, generative AI, or agentic AI solutions.
• Hands-on experience with Microsoft Azure and Azure AI services.
• Experience integrating AI with enterprise systems, ERP, manufacturing, or operational data is a plus.
• Experience leading Agile teams and globally distributed development resources.

Skills

• Technical Skills
  • Azure OpenAI, Azure AI Foundry, Azure Machine Learning, Microsoft Fabric, Copilot Studio, Power Platform.
  • LLMs, RAG, AI agents, prompt engineering, grounding, evaluation, telemetry, and human-in-the-loop patterns.
  • Ability to compare and select fit-for-purpose AI platforms, models, and tools across Microsoft and non-Microsoft ecosystems.
  • MCP, A2A, secure APIs, connectors, cloud architecture, and enterprise integration patterns.
  • Security, identity, governance, MLOps/LLMOps, and regulated-environment awareness.
• Soft Skills
  • Strong communicator who can explain AI concepts to technical and non-technical audiences.
  • Collaborative partner with strong stakeholder management skills.
  • Practical, outcome-focused problem solver who can balance innovation with governance.

Preferred Qualifications

• Microsoft Certified: Azure Solutions Architect Expert.
• Microsoft Certified: Azure AI Engineer Associate (or equivalent GenAI/ML certification).