Enterprise AI Architect

Position Summary

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.
• Practical, outcome-focused problem solver who can balance innovation with governance.

EEO-M/F/D/V

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