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About This Role
### JOB SUMMARY:
The Engineer, AI Strategy \& Solutions is responsible for designing and building AI\-enabled and conventional software systems that integrate with enterprise data platforms, services, and event\-driven architectures to deliver measurable business value. This role translates complex business and technical requirements into scalable, production\-ready solutions—including APIs, data pipelines, and model serving infrastructure (batch and real\-time)—using established platform patterns.
Key responsibilities include implementing AI capabilities such as inference, retrieval\-augmented generation (RAG), and evaluation workflows, while ensuring compliance with Responsible AI principles, security standards, and privacy controls throughout the development lifecycle.
The engineer owns the quality of delivered solutions, including comprehensive testing (unit, integration, end\-to\-end), performance profiling, and observability through logs, metrics, and traces. Participation in on\-call rotations and incident response is expected.
This role collaborates closely with cross\-functional teams including Product, Data Science, Security, and Infrastructure. It involves contributing to design reviews, authoring clear technical documentation and runbooks, and advancing shared libraries, SDKs, and CI/CD/MLOps practices. \#LI\-DNI
### MAJOR RESPONSIBILITIES:
- AI \& Cloud Software Engineering:
+ Provides technical support to project teams on the design, development, and delivery of AI\-enabled and conventional software, translating requirements into designs and working code while aligning to platform standards and patterns.
+ Maintains knowledge of enterprise software/AI standards (architecture, security/privacy, data contracts, responsible AI) and industry best practices.
+ Provides support to project teams to design, build, and deploy AI\-enabled and conventional systems that meet safety, reliability, performance, and compliance requirements.
+ Assists cross\-functional teams and studio leadership to deliver global, multi\-platform software and AI solutions across web, mobile, console, and edge environments.
+ Supports complex delivery across diverse platforms and geographies, aligning technical execution with business goals.
- Cross\-functional quality assurance:
+ Review internal and vendor deliverables—API/architecture docs, data schemas, model cards, security/privacy checklists, test plans (unit/integration/load/perf), and test results—submit redlines/issues and track to resolution.
+ Supports lifecycle technical reviews and readiness gates—requirements and architecture reviews, threat modeling, model card/data lineage checks, test plan definition (offline evals, A/B, load/perf), deployment/go\-live approvals.
+ Ensures alignment with standards and best practices.
- Applied AI Engineering \& Operations:
+ Contribute directly to engineering work: build/integrate services \& APIs, ETL/streaming data pipelines, model training/inference code and RAG/retrieval flows; author automated tests; participate in operational support/on\-call.
+ Supports continuous improvement of engineering processes, templates, and tooling (coding standards, shared SDKs/libraries, CI/CD \& MLOps pipelines, evaluation/observability, incident response).
+ Supports product/production teams with feature breakdown, estimation, and technical risk management.
+ Proactively identify dependencies and risks, facilitate resolution, and maintain momentum across complex initiatives. By bridging product vision with engineering execution, this role drives operational clarity and accelerates value realization.
- AI Platform Engineering \& Developer Enablement:
+ Create and maintain engineering artifacts—design docs, ADRs, runbooks, deployment playbooks, IaC/config.
+ Contribute to shared SDKs/templates and CI/CD/MLOps pipelines; ensure portability, maintainability, reliability, observability, operability, and supportability.
+ Collaborate with engineers across Associate/Engineer/Senior bands; participate in targeted internal workshops (AI solution patterns, secure coding, platform usage, Responsible AI), elevating overall engineering quality and velocity.
+ Ensures that all systems and models are built with integrity, accountability, and resilience from the ground up.
+ Supports and models best in class culture, which promotes innovation, collaboration and problem\-solving. Inspires and motivates teams by leading with optimism and a solution\-oriented approach, drives for results.
- Understand and actively participate in Environmental, Health \& Safety responsibilities by following established UO policy, procedures, training and team member involvement activities.
- Performs other duties as assigned.
### ADDITIONAL INFORMATION:
- Required: Proficient in Python plus one of C\#/Java/Go/TypeScript/C\+\+; experienced with REST/gRPC APIs, microservices, event\-driven architecture, and integration with internal/external services. Works with SQL/NoSQL and data pipelines (ETL/ELT, batch/streaming); familiar with Kafka/Kinesis/PubSub or similar. Deploys on AWS/Azure/GCP using containers/Kubernetes, IaC, and CI/CD; applies MLOps basics (model registry/versioning, feature stores, drift detection). Applies security\-by\-design (authN/Z, secrets, PII handling) and Responsible AI practices (model cards, eval gates); writes maintainable docs and performs effective code reviews.
- Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
- Consistent attendance is a job requirement.
### EDUCATION:
- Bachelor’s degree in a relevant technical field — e.g., Computer Science, Software Engineering, Computer Engineering, Data Science/Analytics, Electrical Engineering (software focus), or Systems Engineering — or equivalent demonstrated skill and experience (e.g., production\-software/AI systems, open\-source contributions, published work) required; or equivalent combination of education and experience.
- Master’s degree in Computer Science, AI/ML, Data Science, Software Engineering, or a closely related field preferred.
- Graduate coursework or certifications in machine learning, distributed systems/cloud, MLOps, security/privacy, or data engineering are a plus.
### EXPERIENCE:
- 5\+ years delivering multi\-platform, networked software (web, mobile, services, edge) to production.
- Hands\-on AI solutioning (e.g., computer vision, NLP/RAG, anomaly detection, personalization) with training \& inference pipelines, evaluation, and monitoring; or equivalent combination of education and experience.
Your talent, skills and experience will be rewarded with a competitive compensation package.
Universal is not accepting unsolicited assistance from search firms for this employment opportunity. All resumes submitted by search firms to any employee at Universal Orlando via\-email, the Internet or in any form and/or method without a valid written Statement of Work in place for this position from Universal Orlando HR/Recruitment will be deemed the sole property of Universal Orlando. No fee will be paid in the event the candidate is hired by Universal Orlando as a result of the referral or through other means.
Universal elements and all related indicia TM \& © 2026 Universal Studios. © 2026 Universal Orlando.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Universal Creative, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Universal Creative AI Hiring
Universal Creative 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% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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