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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
- The Machine Learning domain encompasses the application of algorithms and models to enable computers to learn from and make predictions or decisions based on data. This domain is generally a subset of the AI or Data Domain. Scope includes various subfields, including segmentation, propensity modeling, natural language processing (NLP), reinforcement learning, and working with foundation models for generative AI, agentic AI, and low-code AI solution development.
What you'll do
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.
- Collaborate with leaders, business analysts, project managers, IT architects, technical leads and other engineers, 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 data scientists and product teams to build and deploy machine learning models, focusing on the technical aspects of model deployment
- Implement and optimize Python-based ML pipelines for data preprocessing, model training, and deployment
- Monitor model performance and implement strategies for bias mitigation and explainability. Responsible for ensuring models are scalable and efficient in production environments
- Write and maintain code for model training and deployment, collaborating with software engineers to integrate models into applications
- Partner with a diverse team of experts, leveraging cutting-edge technologies to build scalable and impactful AI solutions
- Build AI agents and automations with low-code platforms such as Microsoft Copilot Studio, Power Automate, and Azure AI Foundry, as well as the Anthropic Claude ecosystem and enterprise data through connectors, APIs, and Model Context Protocol (MCP) servers
- Bridge pro-code and low-code development by packaging ML models and pipelines as reusable services and tools that business teams can consume in Copilot Studio agents and Claude workflows
- Operate as a forward deployed engineer: embed with business and operational teams to identify high-value AI Agentic use cases, rapidly prototype, and take solutions from proof-of-concept to production
- Serve as a trusted technical advisor by translating ambiguous business problems into ML solutions, running demos and workshops, iterating on user feedback, and sharing field learnings with platform teams
All you'll need for success
Minimum Qualifications- Education & Prior Job Experience
- Bachelor's degree in Computer Science, Computer Engineering, Data Science, Information Systems (CIS/MIS), Engineering or related technical discipline, or equivalent experience/training
3+ years of full Software Development Life Cycle (SDLC) experience designing, developing, and implementing large-scale machine learning applications in hosted production environments
3+ years of professional, design, and open-source experience
Preferred Qualifications- Education & Prior Job Experience
- Master's degree in Computer Science, Computer Engineering, Data Science, Information Systems (CIS/MIS), Engineering or related technical discipline, or equivalent experience/training
- 5 years of full Software Development Life Cycle (SDLC) experience designing, developing, and implementing large-scale machine learning applications
- Experience in a forward deployed engineer, solutions engineer, or other customer-facing technical role delivering Agentic solutions directly with business users
- Airline Industry experience
Skills, Licenses & Certifications
- Proficiency and demonstrated experience in: + Python + Databricks + Azure AI Foundry, including Azure ML + Low-code AI platforms: Microsoft Copilot Studio, Power Automate, Power Platform connectors, Dataverse + Anthropic Claude tools: Claude Cowork, Claude Code, custom skills and plugins, Model Context Protocol (MCP) + Database and persistence: Hibernate, Oracle, ORM, query performance tuning + Cloud development: Microsoft Azure, AWS + Web servers: Tomcat, tcServer, WebSphere + Web services: REST/SOAP (JSON/WSDL/XML) + AI/ML frameworks: TensorFlow, PyTorch, Keras, Scikit-learn, Hugging Face + MLOps tools: MLflow, Kubeflow, Airflow, Docker, Kubernetes + Build/deployment tools: Maven, Gradle, Git, JUnit, Mockito + DevOps toolchain: Nexus, SonarQube, GitHub, Jenkins, Elasticsearch, Logback, Log4j, ADO Pipelines
- Proficiency in object-oriented design principles
- Experience implementing responsible AI governance frameworks and controls
- Proficiency in post-deployment model maintenance: observability, monitoring, and drift detection
- Strong understanding of NLP: tokenization, embeddings, transformers, and attention mechanisms
- Knowledge of vector databases (Pinecone, FAISS, Weaviate) and RAG
- Familiarity with data pipelines, ETL, and distributed computing
- Experience building generative AI solutions using LLMs (OpenAI GPT, Google Gemini, Llama)
- Experience building evaluation frameworks for LLMs and AI agents (accuracy, safety, performance)
- Experience building agentic solutions in Copilot Studio (generative orchestration, actions, knowledge sources) and Claude (Cowork, custom skills, MCP integrations), including low-code governance: environments, DLP policies, and security roles
- Proficiency in Microsoft Office (Project, Excel, Word, PowerPoint)
- Experience in Agile methodologies (Scrum)Experience in DevOps practices, including CI/CD
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 Employee 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 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 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. Senior-level AI roles across all categories have a median of $228,700.
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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