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About This Role
Generic Position Summary
As a member of the professional staff, contributes specialized knowledge and skill in artificial intelligence, software engineering, enterprise systems, and business process automation to support team, department, and organizational objectives. Works under limited supervision within established guidelines and technology standards, analyzing complex business and technical information to design, develop, and implement AI\-enabled solutions that improve decision\-making, increase operational efficiency, and enhance enterprise application capabilities.
Specific Job Summary
The AI Engineer supports the organization’s adoption of artificial intelligence across technology and business processes. This position supports with designing, developing, and integrating, testing, documenting and maintaining AI\-enabled software solutions, including large language model\-based applications, agentic workflows, intelligent automation, decision\-support capabilities, and AI integrations within enterprise applications.
The AI Engineer partners works with product owners, architects, software engineering teams, data teams, security teams, and enterprise application owners to identify high\-value AI use cases and translate them into scalable, secure, and maintainable solutions. This role requires software engineering experience, practical knowledge of LLMs and AI application patterns, and the ability to incorporate AI capabilities into existing business systems and workflows.
The incumbent will help evaluate AI technologies, design agent\-based workflows, integrate AI services with enterprise platforms, support responsible AI practices, and contribute to the software development lifecycle. Prior experience working with ERP systems and complex enterprise integrations is preferred.
Specific Expected Contributions
- Develops, configures, tests, and supports AI\-enabled enterprise application solutions that improve automation, decision support, operational efficiency, and user productivity.
- Collaborates in the development and integration of solutions using large language models, retrieval\-augmented generation, agentic workflows, orchestration frameworks, AI services, APIs, and enterprise application platforms.
- Partners with product owners, architects, and technology teams to identify, evaluate, and prioritize AI use cases aligned with business value, risk, feasibility, and technology strategy.
- Translates business needs into functional, technical, and solution design documentation for AI\-enabled applications and intelligent automation workflows.
- Configures and supports agentic workflows that can reason over enterprise information, interact with business systems, invoke tools, and support human\-in\-the\-loop review where appropriate.
- Incorporates Model Context Protocol concepts, tool/function calling patterns, API integrations, and secure enterprise connectors into AI\-enabled software solutions.
- Builds, enhances, and maintains enterprise software components using object\-oriented programming principles and modern application development practices.
- Applies application lifecycle management discipline, including source control, backlog/task management, CI/CD, automated testing, code reviews, release management, and production support practices.
- Works with enterprise application teams to integrate AI capabilities into ERP systems, financial systems, operational platforms, custom applications, and workflow automation solutions.
- Collaborates with data engineering and analytics teams to support AI solution patterns involving structured data, unstructured content, semantic search, knowledge retrieval, data pipelines, and governed enterprise data sources.
- Evaluates AI model behaviors, prompt design, grounding strategies, accuracy, explainability, latency, cost, security, and operational reliability.
- Develops and maintains reusable AI engineering patterns, application components, prompts, evaluation approaches, and technical standards.
- Supports responsible AI practices, including data privacy, security, access control, auditability, transparency, bias mitigation, and appropriate human oversight.
- Collaborates with cybersecurity, infrastructure, and architecture teams to ensure AI\-enabled solutions comply with enterprise security, identity, data protection, and governance requirements.
- Troubleshoots and resolves complex issues involving AI application behavior, integrations, data quality, orchestration, application performance, and production incidents.
- Monitors AI\-enabled solutions for quality, reliability, user adoption, cost efficiency, performance, and business impact.
- Shares technical knowledge, to software engineering teams adopting AI development patterns.
- Stays current with emerging AI technologies, model capabilities, agent frameworks, AI development tools, enterprise integration patterns, and industry best practices.
- Assists in special projects as required.
- Performs other duties as needed.
Education
- Bachelor’s degree in Computer Science, Information Systems or equivalent experience required.
Experience
- At least seven (7\) years of relevant and progressive work experience in software engineering, enterprise application development, systems integration, data engineering, automation, or related technology disciplines.
- Practical experience designing, developing, integrating, or supporting AI\-enabled applications, LLM\-based solutions, intelligent automation, or advanced decision\-support systems.
- Experience with large language models, prompt engineering, retrieval\-augmented generation, embeddings, semantic search, tool/function calling, AI orchestration, or agentic workflow patterns.
- Experience incorporating AI capabilities into enterprise applications, business workflows, operational processes, or internal productivity tools.
- Knowledge of object\-oriented programming, software design principles, API development, application integration, and enterprise application architecture.
- Experience with one or more modern programming languages such as .NET/C\#, Java, or Python preferred. Comparable experience with other enterprise development languages may be considered.
- Experience with all phases of the software development lifecycle, including requirements analysis, design, development, testing, deployment, monitoring, and production support.
- Experience with application lifecycle management practices, including source control, task management, CI/CD, automated testing, release management, and DevOps practices.
- Experience working with ERP systems, financial systems, enterprise platforms, or complex business applications preferred.
- Experience with SQL, relational databases, APIs, data integration, application interfaces, or similar toolsets for analyzing and implementing business rules and data\-driven solutions.
- Experience working in a complex, service\-intensive, deadline\-driven enterprise environment.
- Hospitality, timeshare, finance, accounting, or enterprise business solutions experience preferred.
Skills and Attributes:
- Solid understanding of AI application development patterns, including LLMs, prompt design, grounding, retrieval\-augmented generation, agentic workflows, and AI\-assisted automation.
- Familiarity with Model Context Protocol concepts, AI tool integration, function calling, enterprise connectors, and secure system\-to\-system interactions.
- Proficient in object\-oriented programming, with the ability to design application components that are maintainable, scalable, secure, and testable.
- Proficiency with modern software engineering practices, including API design, integration patterns, source control, code reviews, automated testing, CI/CD, and production support.
- Preferred technical experience with.NET/C\#, Java, and/or Python.
- Knowledge of cloud\-based AI and application services, preferably Microsoft Azure AI services, Azure OpenAI, Azure AI Search, Microsoft Fabric, or comparable platforms.
- Knowledge of ERP systems and enterprise business processes; financial systems experience preferred.
- Solid in SQL and data analysis skills with the ability to understand data structures, business rules, data quality issues, and integration requirements.
- Ability to evaluate AI solution quality, including accuracy, reliability, explainability, performance, cost, security, and operational supportability.
- Understanding of responsible AI principles, data privacy, security, role\-based access, auditability, and governance requirements.
- Demonstrated ability to learn complex business processes and develop technology solutions that address business needs in a holistic and sustainable manner.
- Ability to work effectively with business stakeholders, software engineers, architects, data teams, security teams, and external service providers.
- Strong communication skills with the ability to explain AI concepts, technical designs, risks, and tradeoffs to both technical and non\-technical audiences.
- Solid analytical, problem\-solving, troubleshooting, and critical\-thinking skills.
- Proactive, motivated, disciplined, and comfortable working independently in a fast\-paced, evolving technology environment.
- Effective at prioritizing work, managing ambiguity, and following through on commitments.
- Demonstrated agility in a constantly changing, deadline\-oriented environment.
- Customer\-focused approach with a commitment to delivering secure, reliable, well\-engineered solutions that create business value.
- Ability to provide technical support and guidance to teams adopting AI\-enabled development practices.
Marriott Vacations Worldwide is an equal opportunity employer committed to hiring a diverse workforce and sustaining an inclusive culture.
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 Marriott Vacations Worldwide, 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.
Marriott Vacations Worldwide AI Hiring
Marriott Vacations Worldwide 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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