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About This Role
Sr AI Engineer \- 10917
Fort Worth, TX
In\-person Interviews – Need locals
Description:
Description:
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 will travel the world, grow your ability, and become the best version of you. As you embark on a new journey, you will 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
- Our IT organization is inclusive of many diverse, high\-performing teams dedicated to technical excellence, your role is anchored in supporting our teams on their journey to deliver unrivaled digital products that drive a more reliable and profitable airline.
- Our Delivery Excellence organization is dedicated to improving the success of our development teams through improved daily work habits and alignment to delivery metrics while creating and maintaining resilient and best in class products for our customers and team members. As a Technical Coach at American, you are given the opportunity to guide whole product teams toward achieving company objectives and delivering their product vision more effectively using exemplary and modern engineering 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.
- Coach teams in our immersive dojo / coaching space (the American Airlines “Hangar”)
- Evaluate our product/portfolios and assess their architecture/engineering opportunities.
- Facilitate Value Stream Mapping sessions to identify opportunities in our development and operational value streams
- Identify opportunities to increase flow, eliminate waste, decouple our applications, and enable service\-based ecosystem
- Define/curate leading indicators and metrics to measure progress toward the desired outcomes
- Serve as a go\-to expert on modern technology, architecture, engineering, and DevOps practices.
- Work with teams and leadership to influence and support the engineering culture at American Airlines
- Collaborate with product teams on reaching the next level of customer delivery.
- Be a mentor and get hands\-on with software teams including engineers and architects on learning and applying new ways to solve problems.
- Lead large pairing / mobbing sessions to maximize learning while narrowing focus.
- Design and develop facilitator\-led materials related to modern engineering practices or ways of working.
- Bring your real\-world software engineering experience to the enablement organization to help build a viable community of technical coaches and practitioners throughout the enterprise.
- Embed with teams to learn their environment and behaviors as a precursor to designing impactful coaching interventions.
- Develop and implement coaching interventions for teams including software engineers and architects!
- Exemplify and demonstrate preferred team behaviors and embody the desired culture. That means it’s important also to professionally address undesirable behaviors if they should arise.
- Contribute to the developer experience platform that removes organization friction from the continuous delivery of value.
All you’ll need for success
Minimum Qualifications\- Education \& Prior Job Experience
- Bachelor’s degree in computer science or related discipline or an equivalent combination of education and work experience
- 4\+ years of relevant work experience in an agile environment
- Demonstrated expertise in at least one object\-oriented language.
- Previous experience supporting high performing Agile / DevOps teams.
- Past experience coaching or mentoring others, in any context.
- Hands\-on experience with engineering practices such as object\-oriented design patterns, Extreme Programming, cloud design patterns, TDD, BDD, refactoring, and automation
- Experience with Continuous Integration and Continuous Delivery
- Expertise in Agile methodologies like Extreme Programming, Kanban, or Scrum
- Experience with implementing and operating DevOps tools/platforms across some part of the development lifecycle.
- Working experience deploying and supporting business\-critical, Internet scale distributed systems or high\-volume client/server systems
- Proven experience in SQL and ability to design relational database schemas
- Ability to scrutinize provided architectures and suggest optimizations for deployment and minimizing the cost of change.
Preferred Qualifications\- Education \& Prior Job Experience
- Experience building continuous integration/deployment pipelines.
- Experience with any of the following:
- The Testing Pyramid and how to effectively apply it
\- Multi\-threading and concurrency
\- Debugging, performance profiling and optimization
\- Object\-oriented and service\-oriented application development patterns
- Experience with native/cross\-platform Mobile development
- Adept and comfortable communicating with team members and external business stakeholders of all levels
- Demonstrated ability to find creative ways of improving and simplifying solutions, systems, and processes without getting bogged down in blockers or bottlenecks.
- Experience in modern and classical neural networks including LLMs, CNNs and RNNs, Agentic AI and Autonomous Agents, RAG architecture, and MLOPs
Skills, Licenses \& Certifications
- Proficiency with the following technologies:
§ Programming Languages: Java, Python, C\#, Javascript/Typescript
§ Frameworks: Spring/SpringBoot, FastAPI
§ Front End Technologies: Angular/React
§ Deployment Technologies: Kubernetes, Docker
§ Source Control: GitHub, Azure DevOps
§ CICD: GitHub Actions, Azure DevOps
§ Data management: PostgreSQL, MongoDB, Redis
§ Integration/APIs Technologies: Kafka, REST, GraphQL
§ Cloud Providers such as Azure and AWS
§ Test Automation: Selenium, TestNG, Postman, SonarQube, Cypress, JUnit/NUnit/PyTest, Cucumber, Playwright, Wiremock/Mockito/Moq
- Ability to optimize solutions for performance, resiliency and reliability while maintaining an eye toward simplicity.
- Ability to concisely convey ideas verbally, in writing, in code, and in diagrams.
- Proficiency in object\-oriented design techniques and principles
- Proficiency in Agile frameworks and methodologies
- Proficiency in DevOps Toolchain
Language, Communication Skills, \& Physical Abilities
- Ability to effectively communicate both verbally and written with all levels within the organization
- Physical ability necessary to safely and successfully perform the essential functions of the position, with or without any legally required reasonable accommodations that do not pose an undue hardship. Note: If the Company has reason to question an employee’s physical ability to safely and/or successfully perform the position’s essential job functions, the HR team generally will engage in an interactive process to determine whether a reasonable accommodation is appropriate. HR (working with the operation) ordinarily first speaks with the team member directly and they mutually identify the physical demands of the job that are or may be impacted by the employee’s obvious or known condition. Then, if necessary, HR would request medical documentation from the team member’s treating physician or others to confirm the employee’s ability to perform those essential job functions safely and successfully.
- 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.
- Competencies:
Caring
Collaboration
Development
Results
Future
Must Haves:
\- 8\+ years of experience
\- AI and ML experience
\- Bachelor degree Computer Science
\- Hands\-on SW development experience
\- Coaching experience
\- TDD, Java and/or Python
Comment: The candidate will be working onsite 4 days a week. The candidate can be H1B, if sponsored by the supplier. There is an opportunity for the candidate to convert to FTE. Interviews will be 1 round onsite. Top Mandatory Skills and Experience: \- 8\+ years of experience \- AI and ML experience \- Bachelor degree Computer Science \- Hands\-on SW development experience \- Coaching experience \- TDD, Java and/or Python
Pay: $65\.00 \- $70\.00 per hour
Work Location: In person
Salary Context
This $135K-$145K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At WorkCog, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 24% below the category median. Disclosed range: $135K to $145K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
WorkCog AI Hiring
WorkCog has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Comfort, TX, US. Compensation range: $145K - $145K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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