Interested in this AI/ML Engineer role at Kiewit Corporation?
Apply Now →Skills & Technologies
About This Role
Requisition ID: 181457
Job Level: Senior Level
Home District/Group: DHO Information Technology Group
Department: Technology Group
Market: Corporate Home Office
Employment Type: Full Time
Position Overview
---------------------
Kiewit is seeking an AI Engineer 3 to design, develop, and operate the enterprise AI platform that powers intelligent applications across the organization. This role focuses on building scalable, secure backend AI services, reusable AI capabilities, and platform APIs that application development teams integrate into their solutions. The ideal candidate has experience building cloud\-native backend services and production AI systems using technologies such as Model Context Protocol (MCP), agent\-based workflows, Retrieval\-Augmented Generation (RAG), and enterprise LLM platforms. This engineer independently delivers complex platform capabilities, mentors junior engineers, and collaborates with architects, product managers, and application development teams.
District Overview
---------------------
Kiewit Technology Group builds solutions to enable and support our company's expansive operations. Our mission is to deliver project schedule and cost certainty by employing technology designed by and for the construction industry. Our team utilizes systems and tools that manage every part of Kiewit's business and the project lifecycle to improve planning and day\-to\-day execution in the field. We give our people real\-time data to make faster, smarter decisions.
Location
------------
This is a full time in office role located in LaVista, NE or Lenexa, KS.
Responsibilities
--------------------
- Design, develop, and deploy reusable AI platform services and backend APIs.
- Build scalable backend services that orchestrate enterprise AI capabilities.
- Develop integrations with Azure AI Foundry, Anthropic Claude SDK, Azure OpenAI, and other approved AI providers.
- Design and optimize Retrieval\-Augmented Generation (RAG) services and agent\-based workflows.
- Integrate AI platform services with enterprise data platforms and cloud infrastructure.
- Implement observability, monitoring, logging, and troubleshooting for AI services.
- Optimize platform performance, scalability, reliability, and cost.
- Mentor junior engineers through code reviews and technical guidance.
- Collaborate with architects and application teams on solution implementation.
- Ensure solutions meet security, compliance, and governance standards.
Technologies \& Platforms
- Languages: C\#, Python, Node.js (JavaScript/TypeScript), RESTful APIs
- AI: Azure AI Foundry, Anthropic Claude SDK, Azure OpenAI, MCP, agent workflows, RAG
- Cloud: Azure Container Apps, Azure Functions, Docker
- Data: SQL, PostgreSQL, Azure SQL
- Tooling: LangFuse, Cursor IDE, Git
Qualifications
------------------
- Bachelor's degree in Computer Science, Engineering, or equivalent experience
- 5\+ years of professional software engineering experience
- 2\+ years building production AI services, AI platforms, or reusable AI infrastructure
- Experience building scalable backend APIs and distributed services
- Experience with LLM integrations, MCP, AI workflows, and RAG
- Experience with Docker and cloud\-native container platforms
- Experience building and deploying cloud\-native applications
- Strong SQL and relational database design skills
- Experience mentoring engineers and delivering complex production software
Preferred Qualifications
- Experience with Azure AI Foundry, Anthropic Claude SDK, or Azure OpenAI
- Experience building shared AI platforms, SDKs, or platform APIs
- Experience with LangFuse, Prisma or Drizzle, Express or Fastify Familiarity with React and Shadcn UI
- Knowledge of DevOps, CI/CD, and Infrastructure as Code \#LI\-AK1
Other Requirements:
- Regular, reliable attendance
- Work productively and meet deadlines timely
- Communicate and interact effectively and professionally with supervisors, employees, and others individually or in a team environment.
- Perform work safely and effectively. Understand and follow oral and written instructions, including warning signs, equipment use, and other policies.
- Work during normal operating hours to organize and complete work within given deadlines. Work overtime and weekends as required.
- May work at various different locations and conditions may vary.
We offer our fulltime staff employees a comprehensive benefits package that’s among the best in our industry, including top\-tier medical, dental and vision plans covering eligible employees and dependents, voluntary wellness and employee assistance programs, life insurance, disability, retirement plans with matching, and generous paid time off.
Equal Opportunity Employer, including disability and protected veteran status.
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 Kiewit Corporation, 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. Senior-level AI roles across all categories have a median of $230,000.
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.
Kiewit Corporation AI Hiring
Kiewit Corporation 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, 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.