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About This Role
Kokosing (www.kokosing.biz) is one of America's 50 largest General Contractors and services a broad spectrum of clients in both the private and public business sectors. Kokosing's services include heavy civil/industrial construction such as highways, bridges, underground utilities, water/wastewater facilities, and marine construction. For over 75 years, Kokosing has successfully attracted the most qualified technical personnel in the construction industry by offering visible challenges, superior quality, and attractive rewards. With over $2\.8 billion in annual sales and a commitment to its workforce, Kokosing is the winning team.
Job Description:
We are seeking a results\-driven Chief Data and AI Officer (CDAIO) to lead digital transformation, artificial intelligence (AI), and data innovation across a growing construction organization. Reporting directly to the CEO and serving as a strategic advisor to executive leadership and the Board of Directors, the CDAIO will drive modernization initiatives that enhance operational performance, scalability, profitability, cybersecurity, and competitive advantage.
This executive will lead the transformation of the organization into a digitally enabled, data\-driven enterprise by aligning technology investments with business objectives and delivering measurable outcomes across field operations, corporate functions, and customer experience.
The ideal candidate combines deep proven transformational leadership, technical expertise with exceptional business acumen, and experience scaling technology organizations within complex, multi\-site operational environments.
Organizational Culture
The CDAIO will embody and reinforce the organization’s core cultural values, including:
- Customer\-focus \- We are passionate about building long\-term customer relationships (internal and external). Our culture allows us to respond to our customers with turnkey solutions and quality results. Our goal is to exceed expectations.
- Shirt\-sleeve management \- We believe “None of Us is as Smart as All of Us”. We have a leadership mindset that together we can accomplish anything. We collectively roll up our sleeves and deliver quality results safely, and on time.
- Financially strong \- We make decisions for the long term. We analyze risk and implement smart strategies that allow us to reinvest profits back into the company and secure resources that help make us a better construction partner and industry leader.
Key Responsibilities
Organizational Leadership \& Talent Development
- Build, lead, and mentor high\-performing teams across IT operations, cybersecurity, infrastructure, applications, data engineering, analytics, and AI functions.
- Foster a culture of innovation, accountability, collaboration, inclusion, and continuous improvement.
- Lead organizational change management efforts that support enterprise transformation and technology adoption.
- Develop succession planning and leadership development programs for critical roles.
Strategic Technology \& Business Leadership
- Develop and execute a multi\-year enterprise technology strategy aligned with organizational growth, operational excellence, and long\-term business objectives.
- Serve as the primary technology advisor to the CEO, executive leadership team, and Board of Directors on digital innovation, cybersecurity, AI, data strategy, and technology investments.
- Translate complex technical concepts into clear business value, measurable ROI, and strategic outcomes.
- Lead enterprise\-wide modernization initiatives that improve productivity, collaboration, operational efficiency, and profitability.
Digital Transformation \& Innovation
- Lead large\-scale digital transformation initiatives across construction operations, field technology, enterprise systems, and corporate platforms.
- Modernize legacy systems and drive adoption of cloud\-native, scalable, and integrated technology ecosystems.
- Partner with business leaders to identify and implement technology\-enabled process improvements and automation opportunities.
- Evaluate emerging technologies including Generative AI, machine learning, IoT, predictive analytics, robotics, and intelligent automation.
AI, Data \& Analytics Leadership
- Develop and execute an enterprise\-wide AI and data strategy that positions data as a strategic organizational asset.
- Establish scalable data governance, architecture, analytics, and business intelligence capabilities that support informed decision\-making at all levels of the organization.
- Drive deployment of AI and machine learning solutions that improve forecasting, operational performance, risk management, workforce productivity, and project delivery outcomes.
- Ensure ethical, secure, and compliant use of AI technologies and enterprise data assets.
Enterprise Systems \& Infrastructure Management
- Oversee enterprise applications including ERP, HRIS, CRM, project management, collaboration, and construction technology platforms.
- Lead cloud strategy and infrastructure management across hybrid and multi\-cloud environments.
- Ensure enterprise systems deliver high availability, scalability, reliability, performance, and security.
- Drive adoption of modern engineering practices including Agile, DevOps, automation, and platform standardization.
Cybersecurity, Governance \& Risk Management
- Establish and continuously mature enterprise cybersecurity, IT governance, and risk management frameworks.
- Ensure compliance with regulatory requirements, cybersecurity standards, privacy regulations, and internal controls including NIST, SOC 2, CMMC, and related frameworks.
- Oversee threat detection, incident response, vulnerability management, disaster recovery, and business continuity programs.
- Maintain accountability for the protection of organizational data, intellectual property, and critical infrastructure.
Financial, Vendor \& Operational Leadership
- Develop and manage enterprise IT operating and capital budgets with strong fiscal discipline and ROI accountability.
- Lead technology investment planning, vendor negotiations, strategic sourcing, and contract management.
- Establish KPIs and performance metrics that measure technology effectiveness, operational impact, cybersecurity posture, and business value realization.
- Optimize technology spend while supporting innovation and organizational scalability.
Minimum Qualifications
Experience
- Proven success leading enterprise digital transformation initiatives with measurable business outcomes.
- Demonstrated experience leading enterprise IT strategy, governance, cybersecurity, cloud modernization, and large\-scale systems implementations.
- Experience managing complex, multi\-functional technology organizations in geographically dispersed environments.
- Strong experience presenting technology strategy and investment recommendations to executive leadership and Boards of Directors.
Education
- Bachelor’s degree in Information Technology, Computer Science, Information Systems, Engineering, Cybersecurity, or related field required.
- Master’s degree in Business Administration, Information Technology, Data Science, or related discipline preferred.
Technical Expertise
- Deep expertise in AI/ML, enterprise data platforms, analytics, data governance, and business intelligence.
- Strong knowledge of cloud platforms including AWS, Azure, and/or Google Cloud Platform.
- Experience with enterprise architecture frameworks and scalable infrastructure design.
- Knowledge of ERP, construction management platforms, field technologies, and enterprise integrations.
Certifications Preferred
- CISSP, CISM, CRISC Cloud certifications (AWS, Azure, Google Cloud)
- AI, Data, or Cybersecurity certifications
Preferred Qualifications
- 15\+ years of progressive information technology leadership experience, including 10\+ years in senior or executive leadership roles.
- Experience within the construction, engineering, infrastructure, or asset\-intensive industries strongly preferred.
- Proven track record leading technology integration during rapid growth, mergers, acquisitions, or multi\-site expansion.
- Demonstrated success implementing AI\-enabled operational improvements and data\-driven business strategies.
Critical Success Factors
The ideal candidate will demonstrate:
- Organizational Leadership \& Change Management
- Enterprise Digital Transformation Leadership
- AI \& Data\-Driven Business Innovation
- Executive Influence \& Strategic Partnership
- Scalable Technology \& Cloud Leadership
Core Competencies
- Strategic Technology Leadership
- Digital Transformation
- Artificial Intelligence \& Data Strategy
- Cybersecurity \& Risk Management
- Cloud Architecture \& Infrastructure
- Enterprise Applications \& ERP
- Business Intelligence \& Analytics
- Executive Communication
- Organizational Leadership
- Change Management
- Operational Excellence
Kokosing is an equal employment opportunity/affirmative action federal and state contractor. The company does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected class.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Kokosing Construction, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Kokosing Construction AI Hiring
Kokosing Construction has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Westerville, OH, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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