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About This Role
Job Overview
Under the direction of the Senior Enterprise Data Governance Manager, the Principal AI Lead serves as the strategic and technical authority on Artificial Intelligence (AI) across the organization. This role oversees the definition, governance, and implementation of the enterprise AI strategy, ensuring safe, ethical, and business-aligned AI adoption. The Principal AI Lead is the central point of contact for all AI-related initiatives, responsible for setting direction, evaluating new technologies, piloting use cases, building enterprise capabilities, and establishing controls to prevent unauthorized AI usage. The Principal AI Lead will work across legal, engineering, analytics, and transformation teams to ensure responsible AI innovation at scale.
What you will be doing:
- Defines and drives the organization's AI vision and strategic roadmap, ensuring alignment with enterprise goals, governance and innovation priorities, and technology infrastructure needs.
- Serves as the single point of contact for AI across the organization, guiding teams and leadership on capabilities, risks, compliance, opportunities, and recommending strategic platforms and partnerships (e.g., Snowflake, AWS, Microsoft). Identifies, evaluates, and prioritizes AI use cases, supporting their advancement from ideation to scalable execution with measurable value.
- Defines and track AI-specific KPIs to measure impact, reliability, and alignment of deployed solutions with business outcomes.
- Stays at the forefront of AI trends, integrating emerging technologies, industry standards, and best practices into the enterprise AI strategy.
- Architects enterprise-grade AI and GenAI solutions, works with technical teams to assess infrastructure needs, tool selection, and implementation paths (e.g., RAG, foundational model integration, ML pipelines).
- Leads the selection and adoption of AI tools and platforms (e.g., Azure ML, Dataiku, Snowflake), balancing innovation with governance, scalability, and compliance requirements.
- Establishes and enforce AI governance frameworks that ensures compliance with legal, ethical, and responsible AI usage across all enterprise initiatives.
- Establishes MLOps best practices across the organization and develops reusable components and patterns for ingestion, feature engineering, training, deployment, and monitoring, ensuring model traceability and reproducibility.
- Develops and delivers AI solutions and frameworks, guiding teams through proof of concept to production, while championing reuse, model traceability, and operational excellence.
- Drives organizational readiness and literacy for AI, collaborating with HR, and business units to design training, raise awareness, and support the cultural shift needed for successful AI adoption.
- Performs other related duties as assigned.
What you need:
- Eight (8) years of experience in applied data science, including leading ML/AI initiatives and mentoring teams in a production environment.
- Master’s or PhD in Artificial Intelligence, Computer Science, Data Science, or a related field.
- Proven experience in leading cross-functional AI initiatives and developing governance structures.
Required Knowledge, Skills and Abilities:
AI & Machine Learning Expertise
- Advanced understanding of machine learning, deep learning, and generative AI, including LLMs, transformers, RAG architectures, and model fine-tuning.
- Demonstrated ability to lead AI efforts through strategic direction, platform/tool identification, and collaboration with technical experts.
- Demonstrated ability to move models from experimentation to production, with experience in model evaluation, explain ability, and continuous improvement.
- Hands-on experience designing and implementing MLOps pipelines for training, validation, deployment, monitoring, and retraining.
- Familiarity with tools such as Snowflake AI/ML, Azure ML, and Dataiku.
- Experience working with cloud-based infrastructure.
Business & Strategy Alignment
- Experience Proven ability to translate complex AI concepts into business value and communicate technical findings to non-technical audiences.
- Experience in identifying AI opportunities that align with strategic objectives, quantify ROI, and prioritize initiatives based on impact and feasibility.
Governance & Responsible AI
- Solid understanding of responsible AI principles, including fairness, bias mitigation, transparency, and explain ability.
- Knowledge of AI governance frameworks, model risk management practices, and how to align AI development with compliance and security policies.
- Experience partnering with data governance or compliance teams to ensure ethical use of AI within an enterprise setting.
Leadership & Mentorship
- Track record of mentoring and guiding engineers, or analytics teams through AI solution design and implementation.
- Capable of influencing without authority, leading by expertise, and fostering a culture of innovation and responsible experimentation.
- Skilled at managing multiple initiatives simultaneously while maintaining high standards of technical rigor and strategic alignment.
DESIRABLE:
- Advanced certifications in AI/ML (e.g., AWS Certified Machine Learning, TensorFlow Developer, Microsoft Azure AI Engineer, etc.).
- Experience leading or contributing to the creation of enterprise AI/ML platforms, AI centers of excellence, or federated AI governance models.
- Expertise in Snowflake architecture, including best practices for AI/ML model integration and optimization.
- Familiarity with programming in Python and AI/ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Hugging Face).
- Experience overseeing the development and deployment of AI models (e.g., demand forecasting, classification, recommendation systems, NLP, computer vision) with the ability to evaluate performance, ensure governance alignment, and translate outcomes into business value.
- Comfortable working with cross-functional teams including engineering, analytics, governance, product, and business stakeholders
About Us
We are Dallas Fort Worth International (DFW) Airport-one of the most successful airports in the world by any definition. We are recognized for our innovation, leadership, drive for excellence and talented employees.
Our success is made possible because of the diverse talents of over 2,100 employees who champion our mission of providing an exceptional airport experience to our customers and connect our community to the world. We are looking for the best talent to join us and help reimagine what an airport can be.
Bring your talents to DFW. Live and work with purpose.
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 DFW International Airport, 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.
DFW International Airport AI Hiring
DFW International Airport has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas-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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