Enterprise AI Architect

Position Summary

If the following job requirements and experience match your skills, please ensure you apply promptly.

We are looking to add to our dynamic team the critical role of Enterprise AI Architect 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.

Key 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.

Required Qualifications

Experience

• 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.

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.

Preferred Certifications

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

Soft Skills

• Strong communicator who can explain AI concepts to technical and non-technical audiences.
• Collaborative partner with strong stakeholder management skills. xhyhwjd
• Practical, outcome-focused problem solver who can balance innovation with governance.

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