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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:
Kokosing is seeking a *Construction AI Engineer*. This individual will serve as a strategic partner between Technology and Innovation (T\&I), IT, and business units to support the implementation of intelligent tools, copilots, and agentic workflows to automate processes and solve real\-world problems in the construction industry in alignment with organizational objectives. This individual will bridge the gap between software development and practical/applied AI, integrating predictive models and Generative AI into live project execution and enterprise operations. The Construction AI Engineer will report to the *Director of Construction Technology and Innovation* and have daily interaction with operational leaders and the IT department.
Organizational Culture:
The Construction AI Engineer 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 safely and securely.
- 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. We don’t just watch someone dig the hole; we jump in to help them start digging it.
- 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.
- Innovation mindset \- Innovation is a key focus of our organization and embodies both our cultural mindset in optimizing our business and being a leader in the community and the nation.
Key Responsibilities:
This role focuses on translating business and field needs into working solutions \- from intelligent workflows and copilots to agentic\-based tools and AI\-enabled applications for the construction sector. You will develop robust systems that function reliably within the high\-stakes, risk\-management environment of physical construction. Together, we will build and deliver practical AI solutions that drive automation, productivity, and business value.
- Build AI Agents \& Automations: Design and implement intelligent workflows using LLMs, function calling, agentic AI and multi\-agent patterns, and retrieval architectures.
- System Integration: Integrate AI capabilities into enterprise systems and field applications (e.g., Procore or Autodesk) to support real\-world adoption and measurable value. Partnering with engineers and analysts working on safety, operations, scheduling, and risk to turn agentic capabilities into tools Superintendents, PMs, \& PEs use.
- Testing \& Evaluation: Build rigorous evaluation infrastructure, golden datasets, and testing harnesses for non\-deterministic AI systems to guarantee reliability and compliance.
- Data Management: Work with structured and unstructured construction data, building retrieval systems that span vector search and knowledge graphs over complex project data.
- Stakeholder Collaboration: Partner with domain experts (project managers, superintendents, and safety directors) to refine requirements and turn agentic capabilities into usable daily tools.
- Model Governance: Ensure AI solutions meet corporate standards for security, privacy, and responsible AI practices.
Qualifications \& Skills:
- Education: Bachelor’s or Master’s Degree in Computer Science, Engineering, Data Science, or a related technical field.
- Experience: 5\+ years of experience in hands\-on software engineering or AI/ML engineering, with a proven track record of shipping AI\-enabled applications in a production setting.
- AI \& Orchestration Tools: Hands\-on experience with AI frameworks for orchestration (e.g., LangChain, LangGraph, CrewAI, AutoGen, or LlamaIndex).
- Programming \& Cloud: Strong Python engineering fundamentals and experience with major enterprise cloud platforms and vector databases (e.g., Azure, Databricks, Snowflake, Palantir).
- Construction Knowledge: Knowledge of Architecture, Engineering, and Construction (AEC) technology stacks, Digital Twins, or Building Information Modeling (BIM) is highly preferred.
- Communication: Excellent ability to present technical goals and non\-trivial concepts to non\-technical audiences
Location:
- This role will be based at our corporate office in Westerville, Ohio and will require travel 20% of the time.
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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
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, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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