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About This Role
Who We Are:
*At UES, we’re a team of more than 4,000 engineers, scientists, geologists, inspectors, technicians, and drillers united by a shared purpose—to make a meaningful impact in the communities we serve. As a national leader in geotechnical engineering, environmental consulting, and materials testing and inspection, we collaborate on transformative projects across transportation, energy, water, healthcare, and more. Learn more about the benefits of joining Team UES and our core values at* *careers.teamues.com**.*
Position Overview:
Join UES as an AI Transformation Analyst in Orlando and be the hands‑on force turning manual processes into scalable, AI‑enabled workflows in support of the AI Strategy Director. The Analyst plays a critical role in assisting with the identification, design, and delivery of AI solutions across the organization—bridging business stakeholders and technical teams to transform existing processes through innovative AI and automation.
You’ll be responsible for understanding current‑state workflows, uncovering AI‑driven improvement opportunities, defining business and technical requirements for intelligent solutions, and supporting implementation, deployment, and user adoption. This role operates at the intersection of business analysis, digital transformation, and applied AI.
This is a ground‑floor opportunity on UES’s AI journey: expect direct engagement with end users, high visibility with leadership, and the chance to deliver measurable improvements that impact thousands of employees while accelerating your career in a fast‑growing organization.
Responsibilities:* AI Opportunity Identification \& Process Transformation: Maintain a prioritized pipeline of AI opportunities; map As‑Is and To‑Be workflows; evaluate feasibility, risks, and ROI.
- Project Management: Maintain project plans, trackers, and documentation; monitor progress, surface risks, and prioritize workstreams.
- Requirements Support \& Research: Run discovery sessions, capture action items, draft BRD sections/user stories, and define data and success criteria.
- Solution Design \& Delivery Support: Design process flows and decision logic; translate needs into technical requirements and collaborate with developers.
- Deployment \& Training: Validate deployment/readiness plans, support rollouts and hypercare, and create/deliver training materials.
- Change Management \& Adoption: Prepare communications, gather training feedback, provide go‑live support, and track adoption metrics.
- Testing \& Validation: Define test/UAT scenarios and validate AI outputs, performance, usability, and business alignment prior to release.
Job Responsibilities Note:
*The above statements are intended to describe the general nature and level of work being performed. They are not intended to be construed as an exhaustive list of all responsibilities, duties, and skills required of personnel so classified. UES retains the right to add to or change the duties of the position at any time.*Qualifications:
- Bachelor’s degree in Business, IT, or related field; MBA or CBAP preferred.
- 3–5 years of business analysis experience, including leading projects.
- Strong proficiency in data analysis, business modeling, and process documentation.
- Experience with BI tools, SQL, and workflow/process mapping tools.
- Proven ability to manage stakeholder expectations and drive project success.
Preferred Qualifications:
+ Early‑career (2\+ years, internships/co‑ops count) with strong business analysis, process mapping, and documentation skills.
+ Experience in AEC or professional services strongly preferred.
+ Demonstrated experience deploying or integrating AI in production or backend workflows (Claude or similar models preferred) and defining AI/automation use cases.
+ Relentless AI passion — active self‑learning, experimentation, and staying current on AI trends, tools, and agentic/automation techniques (e.g., Copilot, Claude agents).
+ Able to translate business needs into actionable technical requirements, work with Azure DevOps/Jira and APIs/integrations, and create training/change‑management materials.
+ Willingness to travel for deployments/rollouts (approximately 25% on average; may increase/decrease depending on project).
Travel Requirements:
+ This position requires occasional domestic overnight travel, approximately 25% of the time, to supportbusiness objectives and client engagements. Travel may be both scheduled and on short notice, depending on
project needs. Destinations typically include job sites, field locations, or regional offices, where work may be
conducted in outdoor or operational environments. Adherence to all company safety protocols and use of PPE is
required during travel and on\-site work
Physical Demands \& Work Environment:
+ This is an on\-site role based in our Orlando office and requires regular in\-office attendance.
On\-Site Requirements:
- Primary work location: Orlando office; regular, in‑person collaboration with team members, clients, and stakeholders is expected.
- Typical work involves desk\-based computer and phone use for extended periods; standard office equipment provided in a climate‑controlled environment.
- Physical demands: prolonged sitting, occasional standing/walking/bending, and occasional lifting of materials up to 15 lbs.
- Must be available for on\-site workshops, branch visits, and deployment travel as required.
EEO Statement:
UES is an Equal Opportunity Employer and is proud to recruit the most qualified candidates. Please see our full EEO Statement at the bottom of the page here
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 Universal Engineering Sciences, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
Universal Engineering Sciences AI Hiring
Universal Engineering Sciences has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Orlando, FL, 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
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