Interested in this AI/ML Engineer role at EMERY SAPP & SONS?
Apply Now →Skills & Technologies
About This Role
Senior AI Solutions Engineer
================================
Reports to: EVP, Technology \& Innovation Location: On\-site in Columbia or Kansas City, Missouri preferred.
Why this role exists
------------------------
ESS Companies is a heavy civil construction holding company with six operating subsidiaries. We build roads, bridges, and infrastructure — and increasingly, we build the software and AI systems that make that work faster, safer, and more profitable.
AI and automation are changing how work gets done, and we intend to be deliberate about it rather than swept along by it. This role owns that: finding where AI genuinely helps the business and then making it happen. It is not a research role and it is not a slideware role. It's a builder's role — you'll investigate problems, design solutions, and ship them, sometimes as code, sometimes as a configured tool, sometimes as a vendor you talked us into buying.
You'll be early. You will not be employee \#40 on an established AI team — you'll be the start of one. As the work matures and the function grows, this role has a clear path to leading the team it seeds. You'd work directly with our EVP of Technology \& Innovation to shape what that team becomes and to help build it out. We want someone who can be a strong individual contributor now and grow into that leadership as the function scales — not someone who needs a team underneath them to be effective on day one.
If "define the playbook as you go" sounds like too much ambiguity, this isn't the job. If it sounds like the fun part, keep reading.
What you'll actually do
---------------------------
This role spans four kinds of work. You'll move between them constantly.
1\. Get more out of the tools we already have. We run Microsoft Copilot, Claude, and ChatGPT across the enterprise. Most people use a fraction of what these can do. You'll build agents, projects, and workflows on top of them, write the prompts and guardrails that make them reliable, and help \~3,000 professional staff actually adopt them.
2\. Connect AI to our data. Our analytical source of truth is an enterprise data warehouse, fed from our ERP and other source systems. Our documents live in cloud file\-sharing and collaboration platforms. You'll wire AI tools into these sources — via APIs, connectors, and retrieval pipelines — and expose them as agents and projects people can use without a data engineering degree.
3\. Evaluate and drive commercial (COTS) solutions. Not everything should be built. You'll assess vendor AI products, run honest build\-vs\-buy analyses, and when something makes sense, drive the implementation across the relevant subsidiaries. This requires the credibility to sit in an operations meeting and be believed.
4\. Build custom automation and applications. When there's no good tool to buy, you build it. Our stack is Google Cloud Platform (Cloud Run, Cloud SQL/PostgreSQL, Pub/Sub, Cloud Scheduler, Secret Manager), BigQuery and dbt for data, and a CI/CD\-driven monorepo. AI shows up two ways here: sometimes it's *in* the product (extraction, classification, agents), sometimes it's just how you build faster (we use Claude Code heavily), and often both. You're expected to be fluent in agentic development workflows, not just aware of them.
What we're looking for
--------------------------
- You can actually build. You write production code (Python and SQL at minimum), deploy it, and maintain it. You're comfortable in the cloud — GCP preferred, but we'll take strong AWS/Azure if you can switch.
- You're fluent with modern AI tooling: LLM APIs, prompt design, retrieval/RAG patterns, agent frameworks, and AI\-assisted development (Claude Code, Copilot, or equivalent). You understand where these tools are reliable and, more importantly, where they aren't.
- You can work with data: SQL is second nature, you understand warehouses and ELT, and you can model and query messy real\-world business data.
- You can translate. You can talk to a project manager about job costing, then go write the code, then explain the result to a CFO. The translation is half the job.
- You have good judgment about build vs. buy, and you're not religious about either.
- You operate with minimal direction. You can take a vague business problem, scope it, and come back with something that works.
Nice to have
----------------
- Construction, engineering, or other operations\-heavy industry experience.
- Experience integrating with enterprise systems such as ERP (Viewpoint Vista or similar) and HCM platforms (Workday).
- dbt, BigQuery, and modern data stack experience.
- Experience standing up internal\-facing applications with enterprise auth (Entra ID / OIDC).
- A track record of getting non\-technical people to adopt new tools — adoption is harder than building.
Who thrives here
--------------------
The person who does well in this role is pragmatic over flashy, ships over theorizes, and would rather deliver a credible $100K improvement than promise an unsupportable $1M one. You're comfortable being early, you don't need a playbook handed to you, and you can hold your own with operators who are skeptical of anything with "AI" in the name.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60\-1\.35(c)
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At EMERY SAPP & SONS, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
EMERY SAPP & SONS AI Hiring
EMERY SAPP & SONS has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Columbia, MO, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.