Interested in this AI/ML Engineer role at Teamware Solutions?
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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), reinforcement learning, and generative AI.
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 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.
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
2\+ years of full Software Development Life Cycle (SDLC) experience designing, developing, and implementing large\-scale 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, Technology, Information Systems (CIS/MIS), Engineering or related technical discipline, or equivalent experience/training
5 years of full Software Development Life Cycle (SDLC) experience
Prior experience designing and building agentic systems
1\+ yr prior experience developing and implementing AI solutions using Large\-Language Models (LLMs)
Airline or transport industry experience is a plus
Skills, Licenses \& Certifications
Practical understanding of transformer architectures and how modern LLMs work
Experience building agentic orchestration using frameworks such as Semantic Kernel, LangChain/LangGraph, Microsoft Copilot Studio, OpenAI/Anthropic tool\-calling patterns, or equivalent internal frameworks
Proficiency in prompt engineering: systematic prompt design, few\-shot learning, chain\-of\-thought, and prompt optimization for foundation models
Knowledge of vector databases (e.g., Pinecone, FAISS, Weaviate, Chroma) and RAG architecture patterns
Experience with LLM evaluation methods, including LLM\-as\-a\-judge, automated benchmarking, and regression testing for model outputs
Proficiency in post\-deployment model maintenance, including observability, monitoring, cost optimization, and drift detection
Experience developing and implementing governance frameworks and controls for responsible AI
Familiarity with AI\-assisted development tools (e.g., GitHub Copilot, Cursor, Claude Code) is a plus
Knowledge of experimentation and measurement: A/B testing, KPI instrumentation, and operational analytics
Strong proficiency in Python; working knowledge of Java or another enterprise language
Proficiency in object\-oriented design techniques and principles
Experience with cloud platforms (Azure, AWS, or GCP) and cloud\-native development patterns
Experience with CI/CD pipelines and DevOps toolchain: Git, GitHub, Jenkins, ADO Pipelines, or equivalent
Experience with RESTful API design and integration patterns (JSON, microservices)
Experience in Agile methodologies, such as SCRUM
Proficiency in Microsoft Office Tools (Project, Excel, Word, PowerPoint, etc.)
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\+ 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.
Top 3 Must Haves:
Gen AI Production Development
Python Coding
AI Engineering
Nice to Have Skills:
Coding engineering
Data Science
Candidate must work onsite Tuesdays \- Thursdays; Virtually Mondays/Fridays.
Pay: $50\.00 \- $55\.00 per hour
Application Question(s):
- Are you willing to take Client round as "In\-Person Interview"
- Kindly let us know your Current Location? City and State
Work Location: Hybrid remote in Fort Worth, TX 76155
Salary Context
This $104K-$114K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Teamware Solutions, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($109K) sits 49% below the category median. Disclosed range: $104K to $114K.
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.
Teamware Solutions AI Hiring
Teamware Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Worth, TX, US. Compensation range: $114K - $114K.
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/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 4,317 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).
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 4,317 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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