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About This Role
Intro
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Are you ready to explore a world of possibilities, both at work and duringyour time off? Join our American Airlines family, and you’ll travel the world, grow your expertise and become the best version of you. As you embark on a new journey, you’ll tackle challenges with flexibility and grace, learning new skills and advancing your career while having the time of your life. Feel free to enrich both your personal and work life and hop on board!
Why you'll love this job
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- The Sr Analyst, Business Transformation will be responsible for analyzing and evaluating business opportunities, developing strategic plans, and executing projects to drive growth and profitability. The ideal candidate will have strong analytical skills, project management experience, and excellent communication skills. This role requires a creative, entrepreneurial vision focused on driving operational improvements and change management with strong technical skills.
- This role is part of the Business Transformation team within our Finance division.
What you'll do
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*As noted above, this list is intended to reflect the current job but there may be additional essential functions (and certainly non\-essential job functions) that are not referenced. Management will modify the job or require other tasks be performed whenever it is deemed appropriate to do so, observing, of course, any legal obligations including any collective bargaining obligations.*
- Progress American Airlines’ AI journey across the entire business, helping lead the approach and transformation centrally;
- Build familiarity with AI toolset and be an AI Champion for the business;
- Develop knowledge and awareness of LLM model updates and important industry developments in AI;
- Embed and engage with division leaders to learn the business and build relationships; understand the complexity of business and operational processes and workflows
- Collaborate with business units \& executive leadership to identify and prioritize initiatives that align with overall strategy
- Define project scope, objectives, and timelines for initiatives and track progress against goals
- Manage project stakeholders to execute initiatives and ensure delivery on\-time and within budget
- Monitor and evaluate performance against established KPIs and recommend adjustments as needed
- Assist in development of presentation materials for board meetings \& executive presentations
- Collaborate with executive team members to determine and prioritize strategic growth opportunities
All you'll need for success
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Minimum Qualifications \- Education \& Prior Job Experience
- Bachelors degree in Business or related discipline
- 2\+ years of total experience in management/strategy consulting, investment banking, private equity, corporate strategy, Finance or other related roles
Preferred Qualifications \- Education \& Prior Job Experience
- MBA
Skills, Licenses, \& Certifications
- Excellent project management skills with experience driving strategic initiatives and delivering results
- Strategic planner with the ability to plan and execute upon a defined task or strategy
- Outstanding written and interpersonal communication skills; ability to synthesize and convey complex business concepts effectively
- Self\-starter comfortable working with autonomy, and can successfully prioritize and manage multiple assignments
- Fast learner with a desire to move with urgency, accountability, and intent
- Knowledge and understanding of financial statements, financial modeling, and financial reporting
- Innovative and creative thinker with demonstrated ability to express complex ideas in a clear and concise manner
- Strong emotional intelligence and the ability quickly build internal and external relationships
- Comfort operating in a fast\-paced environment
- Low ego individual with a collaborative and convening work style
- Advanced Excel / Tableau skills and PowerPoint skills to produce board materials and data visualizations
What you'll get
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Feel free to take advantage of all that American Airlines has to offer:
- Travel Perks: Ready to explore the world? You, your family and your friends can reach 365 destinations on more than 6,800 daily flights across our global network.
- Health Benefits: On day one, you’ll have access to your health, dental, prescription and vision benefits to help you stay well. And that’s just the start, we also offer virtual doctor visits, flexible spending accounts and more.
- Wellness Programs: We want you to be the best version of yourself – that’s why our wellness programs provide you with all the right tools, resources and support you need.
- 401(k) Program: Available upon hire and, depending on the workgroup, employer contributions to your 401(k) program are available after one year.
- Additional Benefits: Other great benefits include our Employee Assistance Program, pet insurance and discounts on hotels, cars, cruises and more
Feel free to be yourself at American
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From the team members we hire to the customers we serve, inclusion and diversity are the foundation of the dynamic workforce at American Airlines. Our 20\+ Employee Business Resource Groups are focused on connecting our team members to our customers, suppliers, communities and shareholders, helping team members reach their full potential and creating an inclusive work environment to meet and exceed the needs of our diverse world.
Are you ready to feel a tremendous sense of pride and satisfaction as you do your part to keep the largest airline in the world running smoothly as we care for people on life’s journey? Feel free to be yourself at American.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At American Airlines, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
American Airlines AI Hiring
American Airlines has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Fort Worth, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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