Interested in this AI/ML Engineer role at Texas Capital Bank?
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Texas Capital is built to help businesses and their leaders. Our depth of knowledge and expertise allows us to bring the best of the big firms at a scale that works for our clients, with highly experienced bankers who truly invest in people’s success — today and tomorrow.
While we are rooted in core financial products, we are differentiated by our approach. Our bankers are seasoned financial experts who possess deep experience across a multitude of industries. Equally important, they bring commitment — investing the time and resources to understand our clients’ immediate needs, identify market opportunities and meet long-term objectives. At Texas Capital, we do more than build business success. We build long-lasting relationships.
Texas Capital provides a variety of benefits to colleagues, including health insurance coverage, wellness program, fertility and family building aids, life and disability insurance, retirement savings plans with a generous 401K match, paid leave programs, paid holidays, and paid time off (PTO).
Headquartered in Dallas with offices in Austin, Fort Worth, Houston, Richardson, Plano and San Antonio, Texas Capital was recently named Best Regional Bank in 2024 by Bankrate and was named to The Dallas Morning News’ Dallas-Fort Worth metroplex Top Workplaces 2023 and GoBankingRate’s 2023 list of Best Regional Banks. For more information about joining our team, please visit us at www.texascapitalbank.com.
The Chief Data & AI Officer will executive the data and artificial intelligence strategy across Texas Capital. This role leads the enterprise data and AI strategy, platform, and governance framework, overseeing a team of approximately 100 full-time employees and contractors (40 FTE) spanning data engineering, AI/ML infrastructure, and data governance functions. Reporting directly to the Chief Digital & Information Officer, this executive is responsible for making the bank's data trustworthy, usable, and productive, while turning AI into a governed, repeatable enterprise capability rather than a collection of disconnected experiments. The role requires deep technical fluency across modern data platforms (Kafka, SQL Server, Postgres, Snowflake, Calibra) and emerging AI technologies (agentic systems, LLMs, AI security, AI gateways and guardrails), paired with the executive presence to partner with line of business leaders, technology peers, and the Board.
Responsibilities
- Refine and own the enterprise data and AI strategy, roadmap, and investment case
- Hold accountability for data governance and AI governance across the firm
- Run the Executive Data & AI Council, arbitrating cross-line-of-business definitional and prioritization disputes
- Define the operating model with lines of business, including domain ownership, stewardship, and data products
- Report enterprise data quality, AI risk posture, and realized value to the executive committee and Board
- Own the enterprise data and AI platforms, spanning streaming (Kafka), transactional databases (SQL Server, Postgres), cloud data warehousing (Snowflake), and data cataloging (Calibra)
- Lead development of AI infrastructure supporting LLM deployment, agentic technologies, and generative AI use cases
- Establish and enforce AI security standards, including AI gateways and guardrails to govern model access, usage, and risk exposure
- Drive architectural alignment with the Chief Technology Officer and shared infrastructure teams
- Oversee capacity planning, platform reliability, and cost management across the data and AI estate
- Own AI use-case intake, prioritization, and delivery in partnership with Divisional CIOs, product teams, and lines of business
- Ensure AI initiatives are delivered with measurable business value, appropriate risk controls, and regulatory compliance
- Partner with product and business leaders to identify high-value opportunities for agentic technologies and LLM-driven automation
- Establish a disciplined framework for piloting, scaling, and retiring AI use cases across the enterprise
- Build, lead, and develop a team of approximately 100 full-time employees and contractors (40 FTE) through four direct reports
- Attract and retain top data and AI talent, establishing a culture of accountability, innovation, and operational excellence
- Mentor senior technical leaders to build organizational depth and succession readiness
- Manage a multi-million-dollar technology and talent budget with discipline and transparency
Qualifications
- 15+ years of progressive experience in data, analytics, or AI leadership, including 8+ years managing large, multi-disciplinary teams (50+ employees)
- Advanced degree in Computer Science, Data Science, Engineering, or related field preferred Deep experience in data platforms, governance, and analytics within complex, regulated environments such as financial services
- Hands-on fluency with modern data infrastructure: Kafka or equivalent event-streaming platforms, SQL Server, Postgres, and cloud data warehouses such as Snowflake
- Demonstrated experience governing or deploying AI/LLM technologies, including AI security, AI gateways, and guardrail frameworks
- Proven track record setting enterprise data and AI strategy and translating it into a funded, prioritized roadmap
- Experience running cross-functional governance bodies (e.g., data councils) and arbitrating competing business priorities
- Strong track record of regulatory and compliance exposure (e.g., SOX, data privacy) in a banking or financial institution
- Executive-level communication skills, with experience reporting to executive committees and Boards
- Experience with data cataloging and governance tools such as Calibra
- Background in agentic AI systems and emerging generative AI architectures
- Prior experience operating in a first-line role with second-line risk oversight (e.g., partnering with a Chief Risk Officer on data and model risk)
- Experience building or scaling a data/AI organization through a major technology transformation or operating model change
The duties listed above are the essential functions, or fundamental duties within the job classification. The essential functions of individual positions within the classification may differ. Texas Capital Bank may assign reasonably related additional duties to individual employees consistent with standard departmental policy.Texas Capital is an Equal Opportunity Employer.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Texas Capital Bank, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Texas Capital Bank AI Hiring
Texas Capital Bank has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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