IT AI Engineering Manager

We need to output HTML fragment with sections for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. No Pay or Schedule unless present. The posting includes Benefits section. It includes "A Brief Summary Of This Position" which is about the role. It includes "Essential Functions" which are responsibilities. It includes "Other Important Details About The Role" maybe also responsibilities. It includes "Required Qualifications Of Every Candidate" and "Highly Qualified Candidates Will Also Possess These Qualifications". Those are qualifications. Skills maybe part of qualifications. We can combine qualifications and skills? The instruction: use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Skills). We need to decide which sections to include. Likely: About the role (summary), Responsibilities (Essential Functions + Other Important Details), Qualifications (Required Qualifications + Highly Qualified). Benefits (Benefits). No Pay or Schedule. No Requirements separate? Could treat Requirements as qualifications. We'll include About the role, Responsibilities, Qualifications, Benefits. We must not repeat job title as heading. No Job ID. Remove boilerplate like EEO etc. Also remove navigation etc. We'll keep substantive info. We need to use

for each section heading. Use

• for any list of duties/requirements/benefits. Use

  for prose. We need to preserve all substantive information; do not invent or summarize away detail. So we need to include details from the posting. We'll parse sections: About the role: The brief summary: "This is a management-level position within the Corporate Shared Services IT organization responsible for leading the development, deployment, and operationalization of Artificial Intelligence (AI) and Generative AI capabilities across the enterprise. The Manager, AI Engineering will oversee a team of AI Engineers/Contractors focused on delivering scalable, secure, and business-driven AI solutions that improve operational efficiency, manufacturing performance, quality, supply chain optimization, and employee productivity. This role serves as a key bridge between business stakeholders, Data & Analytics, Digital Manufacturing, Enterprise Architecture, and IT teams to transform business opportunities into production-ready AI products. The position is responsible for establishing AI engineering standards, AI delivery frameworks, ML-OPs practices, governance processes, and reusable enterprise AI capabilities while supporting the organization's AI strategy and roadmap. Lead the enterprise AI Engineering function by delivering secure, scalable, and high-impact AI solutions that enable smarter operations, accelerate innovation, improve manufacturing performance, and create measurable business value across the organization. It is essential to embody Valmont’s core values of Passion, Integrity, Continuous Improvement, and Delivering Results while cultivating a workplace where employees feel valued, trusted, understood, and respected." That's a lot; we can put as

  . Responsibilities: Essential Functions list (bulleted). Also Other Important Details About The Role (list). We'll combine into one responsibilities section with bullet points. Let's extract bullet points from Essential Functions: - This position reports directly to the Senior Director, Data & Analytics, and will have direct reports. - This role requires up to 25% travel (domestic and international), including overnight stays. - Build, mentor, and lead a high-performing AI Engineering team responsible for developing enterprise AI and Generative AI solutions. - Partner with business leaders to identify, prioritize, and deliver AI use cases that generate measurable business value. - Develop and maintain the enterprise AI engineering roadmap aligned with strategic business objectives. - Lead the design, development, testing, deployment, and support of AI-powered applications and intelligent automation solutions. - Establish best practices for AI development, ML-OPs, model deployment, monitoring, and lifecycle management. - Collaborate with Data Engineering teams to ensure AI solutions leverage trusted enterprise data assets from modern cloud data platforms. - Drive implementation of Generative AI solutions, AI assistants, intelligent agents, and Retrieval-Augmented Generation (RAG) architectures. - Develop reusable AI services, APIs, frameworks, and accelerators that enable rapid delivery of business solutions. - Participate in data model and data product discussions to ensure AI requirements are incorporated into enterprise data architecture. - Ensure AI solutions adhere to corporate security, compliance, privacy, and governance standards. - Evaluate emerging AI technologies and recommend opportunities to improve business operations and productivity. - Implement monitoring, observability, and performance management practices for production AI systems. - Apply Agile and DevOps methodologies to support effective AI product delivery and continuous improvement. - Develop and maintain documentation, standards, and operational procedures related to AI engineering capabilities. - Support the advancement of enterprise AI literacy and responsible AI adoption across the organization. Other Important Details About The Role bullet points (some are sentences, not bullet but we can treat as list): - The incumbent must effectively communicate complex technical concepts and AI capabilities to both technical and non-technical stakeholders. - This position acts as a liaison between Manufacturing Operations, Supply Chain, Engineering, Quality, Corporate Functions, and IT to ensure AI initiatives are aligned with business priorities. - The role requires strong leadership and collaboration skills to influence organizational adoption of AI technologies and practices. - Direction is provided by the immediate supervisor and typically consists of strategic priorities, business objectives, enterprise architecture standards, governance requirements, and resource constraints. - The incumbent exercises significant independent judgment in evaluating AI technologies, recommending solutions, and managing technical delivery activities. - Major AI initiatives require broad understanding of business processes, operational challenges, technology capabilities, and organizational impacts. - The position collaborates closely with enterprise architects, cybersecurity teams, data engineering teams, data governance teams, and business leaders. - Responsible for identifying risks associated with AI implementations and developing mitigation strategies to ensure reliable and trustworthy outcomes. - Supports continuous improvement efforts by identifying opportunities to automate processes, enhance decision-making, and improve operational performance using AI technologies. We'll include these as

  • . Qualifications: Required Qualifications Of Every Candidate list (bullet). Also Highly Qualified Candidates Will Also Possess These Qualifications list. Let's extract required qualifications: - Bachelor's Degree in Computer Science, Data Science, Engineering, Information Systems, Mathematics, or a related field, with 8+ years of relevant experience; or equivalent combination of education and experience. - 3+ years of experience leading technical teams and managing technical professionals. - Demonstrated experience developing and deploying AI, machine learning, or advanced analytics solutions in production environments. - Strong knowledge of Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agent architectures. - Experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform. - Strong programming skills in Python and experience with modern AI and machine learning frameworks. - Experience with ML-OPs practices including model deployment, monitoring, versioning, testing, and lifecycle management. - Understanding of software engineering best practices, API development, DevOps, and CI/CD methodologies. - Demonstrated ability to translate business challenges into scalable technology solutions. - Experience working with enterprise data platforms, data products, and modern data architectures. - Excellent communication, presentation, and stakeholder management skills. - Demonstrated ability to lead cross-functional initiatives and influence decision-making across organizations. - Strong analytical, problem-solving, and organizational skills. - High degree of initiative, accountability, and self-motivation. - Ability to work effectively with both technical and business teams. - Passion and integrity with the drive to deliver measurable business outcomes. Highly qualified: - Master's Degree in Artificial Intelligence, Computer Science, Data Science, Engineering, or related discipline. - Experience in manufacturing, industrial, operations, supply chain, or quality-focused environments. - Experience with Azure OpenAI, Azure AI Services, Microsoft Copilot Studio, Databricks, MLflow, LangChain, or Semantic Kernel. - Experience implementing computer vision, predictive maintenance, or industrial AI solutions. - Knowledge of Industry 4.0, Smart Manufacturing, IoT, or Digital Manufacturing initiatives. - Azure, AI, cloud, or data-related professional certifications. - Experience establishing enterprise AI governance frameworks and responsible AI programs. - Experience building or scaling AI Centers of Excellence (CoE). We'll combine into Qualifications section with bullet list. Benefits: The Benefits paragraph includes list items. We'll extract bullet list: - Healthcare (medical, prescription drugs, dental and vision) - 401k retirement plan with company match - Paid time off - Employer paid life insurance - Employer paid short-term and long-term disability including maternity leave - Work Life Support - Tuition Reimbursement up to $5,250 per year - Voluntary programs like tobacco cessation, Type 2 diabetes reversal, one-on-one health coaching, mortgage services and more We'll put as
    • . We must not include Pay or Schedule sections. We must not include boilerplate like EEO etc. We'll omit the EEO paragraph. Now produce HTML fragment: start with maybe but not required. Just output headings and content. We'll do:

      About the role

      ...

      Responsibilities

      • ...
      ...

      Qualifications

      • ...
      ...

      Benefits

      • ...
      ...
      Make sure no extra text. Let's craft the About the role paragraph: combine the summary sentences into one paragraph. We'll need to ensure we don't miss any substantive detail. Let's copy the summary exactly but maybe we can keep as is. Summary: "This is a management-level position within the Corporate Shared Services IT organization responsible for leading the development, deployment, and operationalization of Artificial Intelligence (AI) and Generative AI capabilities across the enterprise. The Manager, AI Engineering will oversee a team of AI Engineers/Contractors focused on delivering scalable, secure, and business-driven AI solutions that improve operational efficiency, manufacturing performance, quality, supply chain optimization, and employee productivity. This role serves as a key bridge between business stakeholders, Data & Analytics, Digital Manufacturing, Enterprise Architecture, and IT teams to transform business opportunities into production-ready AI products. The position is responsible for establishing AI engineering standards, AI delivery frameworks, ML-OPs practices, governance processes, and reusable enterprise AI capabilities while supporting the organization's AI strategy and roadmap. Lead the enterprise AI Engineering function by delivering secure, scalable, and high-impact AI solutions that enable smarter operations, accelerate innovation, improve manufacturing performance, and create measurable business value across the organization. It is essential to embody Valmont’s core values of Passion, Integrity, Continuous Improvement, and Delivering Results while cultivating a workplace where employees feel valued, trusted, understood, and respected." We'll put that in

      . Now responsibilities list: combine both sets. We'll list each bullet as

    • . We need to ensure we don't miss any. Let's list them in order as they appear. Essential Functions bullets (I'll rewrite as concise but preserve meaning): - Reports directly to the Senior Director, Data & Analytics, and will have direct reports. - Requires up to 25% travel (domestic and international), including overnight stays. - Build, mentor, and lead a high-performing AI Engineering team responsible for developing enterprise AI and Generative AI solutions. - Partner with business leaders to identify, prioritize, and deliver AI use cases that generate measurable business value. - Develop and maintain the enterprise AI engineering roadmap aligned with strategic business objectives. - Lead the design, development, testing, deployment, and support of AI-powered applications and intelligent automation solutions. - Establish best practices for AI development, ML-OPs, model deployment, monitoring, and lifecycle management. - Collaborate with Data Engineering teams to ensure AI solutions leverage trusted enterprise data assets from modern cloud data platforms. - Drive implementation of Generative AI solutions, AI assistants, intelligent agents, and Retrieval-Augmented Generation (RAG) architectures. - Develop reusable AI services, APIs, frameworks, and accelerators that enable rapid delivery of business solutions. - Participate in data model and data product discussions to ensure AI requirements are incorporated into enterprise data architecture. - Ensure AI solutions adhere to corporate security, compliance, privacy, and governance standards. - Evaluate emerging AI technologies and recommend opportunities to improve business operations and productivity. - Implement monitoring,