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About This Role
15000 Valmont Plaza Omaha Nebraska 68154 Why Valmont
We’re Here to Move the World Forward.
Valmont impacts millions of people around the world every day, yet they might not realize the many ways. Our technology is helping feed the growing population, supplying the world with more reliable energy and access to renewables, enhancing connectivity in remote and urban locations to create a sustainable future and so much more. Simply put, Valmont is advancing agricultural productivity and reimagining vital infrastructure to make life better.
Join a Fortune 1000 company that respects hard work, honors diversity and invests in our employees as we focus on creating the world of tomorrow, today . We are the modern workforce . Are you ready to move the world forward? Apply now.
A Brief Summary of This Position:
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.
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:
- 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.
Required Qualifications of Every Candidate:
- 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 Candidates Will Also Possess These Qualifications:
- 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).
Benefits
Valmont offers employees and their families a comprehensive Total Wellbeing benefit package to ensure their individual and family’s overall wellness needs are met. Some offerings are dependent upon the role, work schedule, or location and can include the following:
- 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
Valmont does not discriminate against any employee or applicant in employment opportunities or practices on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, veteran status, disability or any other characteristic protected by law. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
If you have a disability and require any assistance in filling out the application for employment email [email protected].
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,653 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At Valmont Industries, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $251,622 based on 51 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Valmont Industries AI Hiring
Valmont Industries has 1 open AI role right now. They're hiring across AI Engineering Manager. Based in Omaha, NE, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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