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About This Role
Overview:
Lamp Rynearson, Inc. is a full-service civil engineering firm of 185 people, delivering projects for public and private clients. We've built our reputation on technical rigor and long-term client relationships, and we're now investing seriously in the technology and AI capabilities that will keep us competitive for the next 20 years.
We're at the point where AI and emerging technology are genuinely changing how civil engineering firms deliver work — and we’re seeking a full-time employee whose primary responsibility is guiding our adoption and incorporation of AI and emerging technology into our daily processes. We're creating a brand-new role, reporting directly to the COO, to lead that effort company-wide. This is a ground-floor opportunity to build a technology function from scratch at a firm that designs the infrastructure people rely on every day.
This is a newly created, company-wide role reporting directly to the Chief Operating Officer — not embedded in a single department or project team. Twelve months in, success looks like: a clear, practical AI and technology roadmap that leadership trusts; several real tools or automations you've built that staff are actually using day to day; a measurable lift in the firm's baseline technology literacy; and a reputation among engineering staff as the person who makes their tools work better — not another mandate from corporate. Qualifications include…
- Bachelor's degree in computer science, software engineering, data science, information systems, or a related field — or equivalent hands-on experience.
- 2 - 10 years in a technology, software development, data, or IT strategy role, including direct experience evaluating and implementing new technology.
- Working proficiency in at least one programming language (e.g., Python, JavaScript/TypeScript, or similar) sufficient to build internal tools and automate workflows.
- Practical, hands-on experience applying AI/ML tools — LLM-based tools, automation platforms, low-code/no-code AI — to real business problems, not just familiarity with the concepts.
- Strong communicator who can translate technical ideas for non-technical staff and translate business problems into technical solutions.
- Comfortable operating as a “team of one” — self-directed, able to set your own priorities, and used to reporting directly to executive leadership. Preferred…
- Experience in AEC (architecture/engineering/construction), civil engineering, or another technical/regulated industry, with familiarity with tools such as Civil 3D, ArcGIS or other GIS platforms, Bentley, or Revit.
- Experience automating engineering workflows (e.g., Dynamo, Grasshopper, Civil 3D API, ArcPy) or integrating engineering software.
- Prior experience in an innovation, digital practice, VDC, or IT strategy role.
- Familiarity with data governance, cybersecurity fundamentals, and responsible AI frameworks.
- Experience designing training or upskilling programs for non-technical staff. Responsibilities…
- Set the firm's AI and emerging technology strategy — evaluate what's genuinely useful versus hype, and build a practical, sequenced roadmap for adoption across engineering, project delivery, and operations.
- Continuously research new software and technology relevant to civil engineering practice — from AI-assisted design and drafting tools to project management, GIS, and data platforms — and make clear build/buy/adopt recommendations.
- Coordinate your activities with our IT Group and our CAD Manager.
- Design, build, and maintain internal tools, scripts, and automations that solve real workflow problems for engineering and operations staff.
- Be the firm's go-to resource for technology and programming questions — hands-on troubleshooting, support, and informal training for staff across every office and discipline.
- Partner with the COO and firm leadership on budget, priorities, and reporting progress and ROI on technology initiatives.
- Establish practical guardrails around data security, responsible AI use, and vendor evaluation so adoption doesn't outrun governance.
- Drive change management and technology literacy across a workforce of engineers who are experts in their field — not necessarily in software.
We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.
Contact:
Human Resources
14710 West Dodge Road, Suite 100
Omaha, Nebraska 68154
Phone: (402) 496-2498
EEO Employer/Vet/Disabled
Location
14710 W. Dodge Rd, STE 100, Omaha, NE 68154
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Lamp Rynearson, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Lamp Rynearson AI Hiring
Lamp Rynearson has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Omaha, NE, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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