An Artificial Intelligence Engineer develops, tests, and deploys artificial intelligence models. May work closely with data software engineers and data professionals to train and implement AI models into existing systems or develop new applications.
AI Enterprise Architect at IT Motives AI Enterprise Architect at IT Motives in Lincolnshire, Illinois Posted in 2 days ago.
Type:
full-time
Job Description:
Please no C2
C or Sponsorship AI Enterprise Architect:
Our amazing partner is a leader in outdoor exploration and retail world. They are looking for an AI Enterprise Architect will lead the strategy, architecture, and delivery of enterprise AI capabilities across the organization. This is a hands-on leadership role responsible for designing, implementing, and scaling production AI solutions that deliver measurable business value. The ideal candidate combines deep technical expertise with strategic thinking and has experience moving AI initiatives from concept to production. This role partners closely with engineering, data, security, cloud, and business leaders to build secure, scalable, and governed AI platforms and applications. We value diversity in the workplace and encourage women, minorities, and veterans to apply. Thank you!
Location:
On-site, Lincolnshire, IL Type:
Contract one year Key Responsibilities Define and execute the enterprise AI architecture strategy and multi-year roadmap. Design scalable architectures for Generative AI, AI agents, machine learning, and intelligent automation. Lead AI initiatives from business requirements through implementation and production deployment. Develop prototypes and reference architectures to validate technical approaches. Establish reusable AI platforms, services, standards, and best practices. Define architecture patterns for LLMs, RAG, AI agents, APIs, integrations, and enterprise data. Partner with engineering teams to ensure AI solutions are secure, scalable, reliable, and production-ready. Establish standards for AI governance, security, observability, evaluation, and operational excellence. Evaluate emerging AI technologies, vendors, and platforms while minimizing technical debt and vendor lock-in. Translate complex technical concepts into business-focused recommendations for executive leadership. Drive adoption of enterprise architecture standards while maintaining a bias toward execution and business outcomes. Required Qualifications Bachelor's degree in Computer Science, Engineering, Information Systems, or related field (or equivalent experience). 10+ years of experience in Enterprise, Solution, or Software Architecture. Proven experience designing and delivering production AI, Generative AI, or Machine Learning solutions. Hands-on experience with Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG), and enterprise AI applications. Strong background in cloud platforms (AWS, Azure, or Google Cloud), APIs, distributed systems, and enterprise integration. Experience with AI platform architecture, data architecture, security, governance, and production operations. Knowledge of LLMOps, MLOps, CI/CD, Infrastructure as Code, and AI lifecycle management. Strong understanding of AI security, responsible AI, and enterprise governance. Excellent communication skills with the ability to influence executives and technical teams. Demonstrated ability to lead AI initiatives from strategy through measurable business outcomes Preferred Qualifications Experience building enterprise AI platforms or Centers of Excellence. Experience with Azure AI, Amazon Bedrock, Vertex AI, Snowflake, Databricks, or similar platforms. Experience with AI agent frameworks, Model Context Protocol (MCP), and multi-agent architectures. Knowledge of Kubernetes, cloud-native architectures, APIs, and event-driven systems. Experience implementing AI governance, FinOps, observability, and enterprise security controls. Relevant cloud, AI, architecture, or security certifications. What Success Looks Like AI solutions successfully deployed into production with measurable business impact. Standardized, scalable AI platforms and reusable architecture patterns. Reduced technical complexity and improved governance across AI initiatives. Strong collaboration between business, engineering, data, and security teams. Faster delivery of secure, reliable, and enterprise-ready AI capabilities
