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About This Role
Posted Date 8/14/2026
Description
Job Description
#### Intro
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
- 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.
- The AI Engineering domain is an area focused on development, deployment, management, and maintenance of Agentic software applications that support business processes and user needs while ensuring reliability, performance, and security. This includes development, application lifecycle management, requirement analysis, QA, security \& compliance, and maintaining the applications and infrastructure while following responsible AI practices.
#### 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.*
- Ensure agent designs meet requirements for reliability, latency, cost efficiency, and maintainability in production environments.
- Exhibits strong business and leadership skills, deep technology perspective to guide solutions, and strong communication skills to coordinate with Engineers and Engineering Managers to ensure they understand business needs and objectives, as well as technical requirements of products
- Collaborates with Business and IT leadership to execute and communicate organization’s technical vision, mission, and work priorities, and drives progress towards roadmaps and ensures efficient technical product management and effective communication with stakeholders
- Implement safeguards for privacy, security, and responsible AI: data handling, access controls, content filtering, audit logging, and human\-in\-the\-loop controls where appropriate
- Supports an environment for engineering teams to be self\-governing organizations who are accountable for their own performance and delivery commitments and measured accordingly
- Communicates across engineering teams to relay business and IT initiatives, priorities, and support clear adherence to business priorities
- Leads, develops, and mentors leaders and top performers across the engineering teams to build next generation of leaders
- Responsible for budget management, forecasting, resource allocation, and long\-term planning to drive alignment with business priorities
#### All you'll need for success
Minimum Qualifications\- Education \& Prior Job Experience
- Bachelor's degree in Technology, Computer Science, Information Systems, or related technical discipline, or equivalent experience/training
- 7\+ years of experience participating in delivery of solutions using ITIL / Agile / XP, or similar methodologies
- 5\+ years of experience leading product teams
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
- Strong understanding of LLM\-based systems, including agent patterns, RAG, tool/function calling, prompt engineering, and evaluation method
- Proven experience delivering production AI solutions with measurable business outcomes
- Experience building agentic orchestration using frameworks such as Semantic Kernel, LangChain/LangGraph, Microsoft Copilot Studio, OpenAI/Anthropic tool calling patterns, or equivalent internal frameworks
- Airline industry experience, including engineering / business processes and supporting technology
Skills, Licenses \& Certifications
- Proven track record in leading Engineering initiatives that generate substantial value; well organized, able to multi\-task, able to prioritize with minimal direction
- Hands\-on expertise in backend engineering (e.g., Python, Java, C\#, or Node.js) and API/service design
- Strong technical leadership, and extensive experience in Agile methodology\-style leadership environment
- Excellent communication skills to present to executive management often and emphasize the important intersection of business and technology
- Proven ability to handle multiple products/work streams and demands efficiently
- Experience in leading a team of individuals with various levels of skills and experience in potentially high stress and challenging situations
- Demonstrated initiative, flexibility, and ability to adapt to changing priorities and work environment
- Leads, influences, and upholds technology standards to ensure resiliency goals are met and continuously improved upon
- Good understanding of financial practices
#### 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.
Type
Full\-time
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,317 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At Information Technology Senior Management Forum, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $244,000 based on 23 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 21 open AI roles right now. They're hiring across AI/ML Engineer, AI Engineering Manager, AI Product Manager, Data Scientist. Positions span Basking Ridge, NJ, US, Fort Worth, TX, US, New York, NY, US. Compensation range: $154K - $413K.
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 Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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