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About This Role
Intro
Are you ready to explore a world of possibilities, both at work and during your 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
- As one diverse, high-performing team dedicated to technical excellence, you will focus relentlessly on delivering unrivaled digital products that drive a more reliable and profitable airline.
- AI domain encompasses disciplines and job families dedicated to the creation, implementation, and management of artificial intelligence technologies. This domain includes roles focused on the research, design, engineering, application, and governance of AI systems. Within the AI domain, professionals work on developing intelligent algorithms and data-driven solutions to solve complex problems and improve decision-making processes. The scope of AI covers various subfields, including natural language processing (NLP), computer vision, robotics, reinforcement learning, and generative AI, agentic AI, and low-code AI development.
What you'll do
- Collaborate with leaders, business analysts, project managers, IT architects, technical leads and other developers, along with internal customers, to understand requirements and develop needs according to business requirements for AI solutions
- Maintain and enhance existing enterprise services, applications, and platforms using domain driven design and test-driven development
- Troubleshoot and debug complex issues; identify and implement solutions
- Create detailed project specifications, requirements, and estimates
- Research and implement new AI technologies to enhance current processes, security, and performance
- Work closely with software architects and technical leads to ensure decisions meet long-term enterprise growth needs
- Work on pioneering projects that harness the power of artificial intelligence to solve complex problems and create significant business value.
- Responsible for participating in all phases of the development process, following agile processes and test-driven development, and using object-oriented development tools to analyze, model, design, construct and test reusable objects.
- Partner with a diverse team of experts, leveraging cutting-edge technologies to build scalable and impactful AI solutions.
- Build and deploy AI agents and workflow automations using low-code platforms such as Microsoft Copilot Studio and Power Automate, as well as the Anthropic Claude ecosystem (Claude Cowork, Claude Code, skills, and plugins), connecting them to enterprise data through connectors and Model Context Protocol (MCP) servers
- Help create reusable templates, prompts, and guardrails that enable business teams and citizen developers to build AI assistants and automations safely
- Work in a forward deployed model alongside business and operations teams (e.g., Airport, Customer, Tech Ops, Finance) to understand their workflows, rapidly prototype AI solutions, and help move them from pilot to production
- Support demos, workshops, and user feedback sessions; iterate quickly and share field learnings with platform and product teams
All you'll need for success
Minimum Qualifications- Education & Prior Job Experience
- Bachelor's degree in Computer Science, Computer Engineering, Technology, Information Systems (CIS/MIS), Engineering or related technical discipline, or equivalent experience/training
- 1+ years of full Software Development Life Cycle (SDLC) experience designing, developing, and implementing large-scale applications in hosted production environments
- 1+ years of professional, design, and open-source experience
Preferred Qualifications- Education & Prior Job Experience
- Master's degree in Computer Science, Computer Engineering, Technology, Information Systems (CIS/MIS), Engineering or related technical discipline, or equivalent experience/training
- 3 years of full Software Development Life Cycle (SDLC) experience
- Prior experience developing and implementing applications with optimization and/or machine learning models inside
- Prior experience developing and implementing generative-based AI solutions using Large-Language Models (LLMs) (e.g., Open AI GPT, Google Gemini, Llama)
- Prior experience building agents or automations with low-code platforms such as Microsoft Copilot Studio, Power Platform, or Claude Cowork
Skills, Licenses and Certifications
- Proficiency in Full Stack Development
- Proficiency and demonstrated experience in the following technologies:
- J2EE technologies: Java, JSP, JMS, JAXB, JDBC, EJB
- Database and persistence frameworks: Hibernate, Oracle, Object/Relational Mapping, Query performance tuning Cloud-based development: Azure Cloud, AWS
Web Servers: Tomcat, tcServer, Websphere, Web Services: REST/SOAP (JSON/WSDL/XML), Frameworks: Spring Framework, Spring Boot
- Front-end: Angular JS, CSS, jQuery, AJAX
- Build/deployment tools: Maven, Gradel, Git, Junit, Mockito
- Other Dev Ops Toolchain: Nexus Repository, SonarQube, Slack, GitHub, Jenkins, ElasticSearch, Logback, Log4j,, ADO Pipelines, Docker
- Other: Linux/Unix shell Scripting, JavaScript, IBM MQ/Rabbit MQ, , SQL Developer, IDE
- Proficiency in object-oriented design techniques and principles
- Proficiency in Retrieval Augmented Generation (RAG) techniques for generative-based AI models
- Experience with Microsoft Copilot Studio, including topics, actions, generative orchestration, knowledge sources, Power Platform connectors, Dataverse, and publishing agents to Teams and Microsoft 365 Copilot
- Familiarity with Anthropic Claude tools such as Claude Cowork, Claude Code, custom skills and plugins, and MCP for connecting Claude to enterprise tools and data
- Understanding of low-code lifecycle management and governance: environments, solution packaging, DLP policies, and security roles
- Strong communication skills, with the ability to explain AI concepts to non-technical business partners
- Experience in DevOps Toolchain methodologies, including Continuous Integration and Continuous Deployment
What you'll get
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 Member Assistance Program, pet insurance and discounts on hotels, cars, cruises and more
Feel free to be yourself at American
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+ Team Member 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
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, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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