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About This Role
A Principal AI Engineer serves as the foremost technical authority and strategic leader for our AI discipline. This is role is an engineer who combines profound technical expertise with the ability to influence and guide the entire organization's technical direction.
The Principal AI Engineer will be entrusted with our most complex, ambiguous, and highest\-impact technical challenges. You will operate across multiple teams and business units, setting the long\-term vision for our AI architecture, platforms, and practices. You will be a force\-multiplier, mentoring our most senior technical talent and ensuring that the solutions we build today are the foundation for our market leadership tomorrow.
Your Day to Day:
- Define and drive the long\-term technical strategy for AI at the company. Identify and champion new technological paradigms that create step\-change opportunities and sustainable competitive advantages.
- Design, and prototype foundational AI platforms, frameworks, and core services that will be leveraged by engineering teams across the entire organization. Your designs will set the standard for scalability, security, and innovation.
- Act as the lead technical consultant on AI for all product and engineering teams. Influence architectural decisions, development practices, and technology choices to ensure a cohesive and forward\-looking technical ecosystem.
- Tackle the most complex and mission\-critical technical problems that are beyond the scope of a single team. This includes pioneering new model architectures, solving novel systems\-level problems, and leading high\-risk, high\-reward R\&D initiatives.
- Act as a dedicated mentor and role model for our Lead and Senior engineers. Grow the next generation of technical leaders by elevating the architectural and design skills across the company.
- Establish and evangelize best practices, design patterns, and engineering standards for AI development, from model training and deployment to ethics and reliability.
What We Need From You:
Education
- Bachelor’s or Master’s degree in a relevant field of work or an equivalent combination of education and work\-related experience.
Experience
- Typically, a minimum of 10\+ years of progressive work\-related experience with demonstrated proficiency in multiple disciplines, technologies, or processes related to the position.
- Experience working with a set of geographically dispersed team and bringing a holistic view of development projects.
- A distinguished career with extensive experience in software engineering and AI, with a proven track record of operating at a Principal or equivalent level (e.g., Senior/Lead Staff Engineer, Architect).
- Recognized as a technical leader with experience setting the technical vision for multiple teams or an entire engineering department.
- Verifiable history of architecting, building, and shipping large\-scale, mission\-critical AI systems that have delivered significant business impact.
- Deep experience navigating ambiguity and translating high\-level business or product strategy into a concrete, multi\-year technical roadmap.
- Experience presenting to and influencing executive leadership (VPs, CTO, CPO) on complex technical topics.
Technical Skill \& Knowledge
- Expert understanding of modern AI concepts, including Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), and agentic workflows.
- Deep, authoritative knowledge of major generative AI platforms. Proven ability to reason about and architect solutions using the Google Gemini family of models and their underlying infrastructure is highly preferred.
- Expert\-level proficiency in Python and common AI/ML libraries (e.g., TensorFlow, PyTorch, LangChain, Hugging Face).
- Expert experience with a major cloud provider (GCP, AWS, Azure), including their AI/ML services. Familiarity with MLOps principles and tools for CI/CD, model deployment, and monitoring.
- Solid understanding of microservices architecture, API design (e.g., REST, gRPC), and containerization technologies (e.g., Docker, Kubernetes).
- World\-class ability to design highly scalable, fault\-tolerant, and elegant distributed systems. You don't just use the cloud; you architect for it.
- Strong analytical and problem\-solving skills
- Ability to display effective verbal and written communication skills when explaining complex technical issues to a variety of technical audiences, including clients, vendors, senior management and staff.
- A history of contributions to open\-source projects, publications in reputable conferences, or speaking at technical events is a strong plus.
- Ability to establish and maintain a high level of customer trust and confidence in the software engineering team’s knowledge of the customer’s business needs.
- Understands and implements work effectively utilizing agile best practices.
Travel \- 10%
Location \- Our hybrid work structure is an expectation of three (3\) days a week in office. This expectation may be adjusted to evolve with the changing needs of the business.
The salary range for this role is $180,000 to $220,000\. This role is also eligible for bonus pay. We offer a comprehensive package of benefits including paid time off, medical/dental/vision insurance, 401K, and other benefits to employees.
\#LI\-ZY1
At IHG Hotels \& Resorts, we work together to deliver True Hospitality for Good on a global scale. With corporate offices and over 6,000 hotel destinations worldwide, a career at IHG is the perfect way to broaden your horizons. You’ll experience our unique culture and brilliant colleagues who will support and inspire you. With a host of corporate opportunities to choose from, wherever you are on your career journey, and whatever you want to achieve there’s Room for You at IHG.
Over recent years, we’ve transformed our company. We have bold ambitions to drive performance and maintain our relentless focus on growth in order to be the hotel company of choice that guests \& owners love.
We are a hospitality business at our core and value connections and being together helps us foster a unique sense of belonging that also supports productivity. That’s why here at IHG, we give our colleagues flexibility and balance – working in a hybrid way, blending office and remote working collectively. We recognise that every role is different, that’s why leaders work with teams to determine how and when they collaborate.
We provide a wide range of benefits designed to help you live your best work life. These include impressive room discounts across our many properties, recharge days and volunteering days throughout the year. Through our myWellbeing framework, we are committed to supporting wellbeing in your health, lifestyle, and workplace. We offer a unique and inclusive culture, where there is always Room for You to belong, grow and make a difference.
Our mission is to welcome everyone and create inclusive teams where we celebrate difference and encourage colleagues to bring their whole selves to work. IHG Hotels \& Resorts provides equal employment opportunities to applicants and employees without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other category protected by applicable laws. We promote a culture of trust, support, and acceptance. Always welcoming different backgrounds, experiences, and perspectives.
Don't quite meet every single requirement, but still believe you'd be a great fit for the job? We'll never know unless you hit the 'Apply' button. Start your journey with us today.
Important information:
- The salary range listed is the lowest to highest pay scale we, in good faith, believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the posted range, and the range may be modified in the future. An employee’s pay position within the pay range will be based on several factors, including relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, shift, travel requirements, sales or revenue\-based metrics, and business or organizational needs.
- No amount of pay is considered to be wages or compensation until it is earned, vested, and determinable. The amount and availability of any bonus, commission, or other form of compensation allocable to a particular employee remain in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.
- If you require reasonable accommodation during the application process, please click here.
- IHG does not accept applications, inquiries, or unsolicited CVs/resumes from staffing or recruiting agencies. Please click here for our agency policy.
- If you are a resident of or applying to a job opening in the State of Washington, please click here to read about applicable benefits.
Salary Context
This $180K-$220K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At IHG Hotels & Resorts, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($200K) sits 20% above the category median. Disclosed range: $180K to $220K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
IHG Hotels & Resorts AI Hiring
IHG Hotels & Resorts has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $180K - $375K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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