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Job Description:
POSITION SUMMARY
The Senior Artificial Intelligence (AI) Engineer serves as a technical lead for designing, developing, and supporting AI-based solutions that deliver measurable business value. This high-impact position helps guide solution design, development standards, responsible AI practices, and assists with the selection of leading technologies and implementation approaches for AI initiatives. The role partners closely with internal teams and external technology partners to deliver secure, scalable, and reliable AI solutions that translate business needs into practical technical outcomes.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
- Lead technical design, development, testing, deployment, and ongoing support of AI-based solutions, including intelligent applications and AI agents.
- Provide technical guidance across AI projects by establishing solution architecture, development standards, best practices, and implementation approaches.
- Collaborate with business and technical stakeholders to define use cases, translate requirements into scalable AI solutions, and ensure successful delivery.
- Partner with the Data and Analytics team to identify, prepare, and operationalize data needed to support AI models and intelligent workflows.
- Work closely with the IT Infrastructure team to ensure AI solutions are deployed in reliable, scalable, and maintainable environments.
- Coordinate with the Security team to help implement monitoring, governance, and control mechanisms for AI solutions, including controls related to AI agents, model behavior, access, and data protection.
- Support requirements gathering and solution alignment with the requirements management team and cross-functional subject matter experts.
- Evaluate and integrate appropriate tools, frameworks, and cloud capabilities to improve AI solution performance, maintainability, and security.
- Collaborate with specialized technology partners to accelerate delivery, validate architecture decisions, and implement best practices.
- Document solution designs, technical decisions, and operational support guidance for AI implementations.
- Stay current on emerging AI technologies, engineering practices, and governance considerations, and recommend practical improvements where appropriate.
- Perform other duties as assigned by the Sr. Director of Information Technology & Systems.
Cross-Functional Collaboration
- Act as a liaison between IT and business teams to ensure solutions support business needs.
- Provide technical guidance and recommendations to business stakeholders regarding system capabilities and best practices.
- Participate in cross-departmental initiatives that require system design and development or data integration.
Compliance, Reporting, and Data Governance
- Assist in maintaining appropriate system documentation, integration documentation, and technical standards.
REQUIRED KNOWLEDGE, SKILLS, AND ABILITIES:
- Bachelor’s degree in Computer Science, Information Systems, or related field; or an associate degree with relevant experience; or an equivalent combination of education and professional experience.
- Minimum 7+ years of software development, process automation, data engineering, or related technical experience.
- 3+ years of experience developing and implementing AI-based solutions deployed and used in production environments.
- Strong software development experience with the ability to design, build, test, and support robust technical solutions.
- Experience working with AI/ML, generative AI, natural language processing, or intelligent automation solutions.
- Experience monitoring, conducting due diligence, and recommending advanced technologies that align with strategic plans and the technology roadmap.
- Ability to lead technical workstreams and collaborate effectively across multiple teams and stakeholder groups.
- Strong problem-solving and communication skills.
- Experience developing and supporting API-based integrations (SOAP and REST).
- Strong analytical, troubleshooting, and problem-solving skills.
- Strong written and verbal communication skills with the ability to work effectively with both technical and non-technical stakeholders.
- Ability to manage multiple priorities in a fast-paced and evolving technology environment.
- Demonstrated customer-focused mindset with strong interpersonal skills and a collaborative approach.
PREFERRED KNOWLEDGE, SKILLS, AND ABILITIES:
- Experience with Microsoft Synapse and/or Fabric analytics platforms.
- Familiarity with Microsoft collaboration and productivity tools including Teams and SharePoint.
- Experience with Microsoft 365 applications.
- Experience with SQL development.
PHYSICAL DEMANDS:
The physical demands described are representative of those required of any employee to perform the essential functions of this role. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. While performing the duties of this job, the employee frequently communicates with employees and clients and must be able to exchange accurate information. Additional functions include standing and/or sitting in a stationary position for extended periods of time; 50%+ of the time, using a computer/laptop in an office environment with natural and/or fluorescent lighting; navigating between office building floors; regularly attending off-site meetings or events; and lifting up to 35 lbs. unassisted.
WORK ENVIRONMENT:
The work environment characteristics described here are representative of those an employee may encounter while performing the essential functions of this job. Work is primarily performed in a typical office environment, with occasional jobsite travel required. Jobsite visits may involve uneven terrain, stairs or ladders, variable weather, noise, dust, and moving equipment. Required PPE must be worn as directed, including a hard hat, safety glasses, high-visibility vest, gloves, hearing protection, and safety-toe footwear. The employee frequently works alone and with others.
Additional Job Description:
Base Compensation: $116,000 - $157,500
The base salary range may vary depending on geographic location, applicant skills, and prior relevant experience.
We offer our full-time employees a comprehensive benefits package that includes medical, dental, and vision plans, life insurance, disability coverage, retirement plans with matching contributions, and generous paid time off. Additionally, we provide profit sharing, tuition reimbursement, and an employee assistance program.
Who Are We?
Veit is one of the country’s leading specialty contractors, with nearly a century of experience and the best people in the industry. We specialize in foundations, earthwork, utilities, marine services, industrial cleaning, cured‑in‑place pipelining, and waste disposal. We pride ourselves on our relentless pursuit to push the construction industry forward and deliver for our customers. Our modern fleet of iron, diverse expertise, and unmatched determination allow us to successfully tackle some of the largest and most complex projects across every sector while also living out our values every day. Our company culture is one where everyone pitches in to get the job done, regardless of job title or seniority. We do everything we can to maintain an atmosphere where family comes first. We want every one of our employees to look forward to each day at work and make it home safe and happy at the end of the day.
Never settle, dig deeper
Our Core Values
- Deliberately Safe We believe all safety incidents are avoidable when everyone operates with a deliberate mindset. We are deliberately proactive and thorough in training and job planning. We are deliberately safe in our everyday operations and decision-making.
- Build with Intention We build with intention. Whether in the office or the field, we work with grit and determination toward a better future for everyone involved. We leave things better than we found them, and never leave a job until we’re proud to put the Veit name on it.
- Leaders Dig First Egos have no place at Veit. We expect everyone on our team to dig in with a leadership mentality. That means being willing to jump in, take ownership, and get mud on our boots — especially when times get challenging.
- Real Relationships Like our work, we approach relationships authentically, with a long-term mindset. Whether you’re a client partner, vendor, or team member, we make an effort to build a more meaningful connection with you by communicating with respect and honesty.
- Integrity in Everything As part of the Veit family, everything we do and say reflects on this entire company. We act with integrity, which means treating others just as we want to be treated, doing the right thing, and holding ourselves accountable.
VEIT IS AN EQUAL OPPORTUNITY AFFIRMATIVE ACTION EMPLOYER, INCLUDING VET/DISABILITY
Salary Context
This $116K-$157K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At VEIT, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($136K) sits 35% below the category median. Disclosed range: $116K to $157K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
VEIT AI Hiring
VEIT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rogers, MN, US. Compensation range: $157K - $157K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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