AI ML Engineering Visa Sponsorship Jobs in Texas
Texas is one of the top states for AI and ML engineering visa sponsorship, with major hiring concentrated in Austin, Dallas, and Houston. Companies like Dell Technologies, AT&T, and a growing cluster of AI-focused startups and enterprise tech firms regularly sponsor H-1B and other work visas for qualified machine learning engineers and AI researchers.
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INTRODUCTION
At ERCOT, our diverse and dynamic work environment provides a platform on which employees can work together to build the future of the Texas power grid and wholesale market utilizing the latest technologies and resources. We encourage you to join our talented, dedicated workforce to develop world-class solutions for today and tomorrow’s energy challenges while learning new skills and growing your career.
ERCOT is committed to fostering inclusion at all levels of our company. It is the cornerstone of our corporate values of accountability, leadership, innovation, trust, and expertise. We know that individuals with a wide variety of talents, ideas, and experiences propel the innovation that drives our success. An inclusive and diverse workforce strengthens us and allows for a collaborative environment to solve the challenges that face our industry today and in the future.
JOB SUMMARY
Leads the team responsible for developing, deploying, and operating machine learning models, generative AI applications, autonomous agents, and related AI solutions across ERCOT’s enterprise platforms. Oversees MLOps standards, production support, platform reliability, and governance for ML and GenAI assets. Balances delivery of new AI capabilities with operational excellence and ensures compliance with AI governance and model lifecycle controls. Partners closely with Data Operations, Data Engineering, Governance, Security, and business stakeholders to ensure safe, reliable, and efficient AI systems.
JOB DUTIES
- Responsible for hiring, coaching, training, and performance management of staff.
- Frequently interacts with reporting supervisors, customers, and/or functional peer group managers, normally involving matters between functional areas or customers.
- Responsible for the management of subordinate staff within a department. Typically has individual contributors as direct reports, but could have supervisory direct reports. Has full responsibility for direct reports.
- Generally provides input to budgeting and financial decisions that impact the department. Requests approval for financial actions beyond a limited scope.
ADDITIONAL JOB DUTIES
- Oversee end‑to‑end delivery of AI/ML and GenAI solutions, from design through deployment, ensuring enterprise‑ready quality, reliability, and security.
- Set technical direction and architectural standards for ML models, GenAI applications, autonomous agents, RAG systems, multimodal solutions, and vector/semantic search capabilities.
- Own and govern MLOps standards, including CI/CD automation, deployment pipelines, monitoring, evaluation frameworks, and model lifecycle controls for both ML and GenAI assets.
- Lead and develop the AI & ML Engineering team, including hiring, onboarding, coaching, performance management, and establishing clear skill ladders and growth pathways.
- Manage production ML/GenAI operations and Level 3 support, leading root‑cause investigations, incident command, post‑incident reviews, and long‑term problem management.
- Ensure compliance with ERCOT model governance and GenAI‑specific controls, including risk tiering, documentation, lineage, prompt management, safety guardrails, and regulatory requirements.
- Guide platform engineering for AI/ML infrastructure, including Azure ML, Databricks ML, vector databases, LLM orchestration frameworks, and ML/GenAI observability tooling.
- Plan and prioritize intake, releases, and roadmaps for ML and GenAI initiatives in partnership with Product Owners and Data Operations leadership.
- Oversee vendor and contractor contributions to ensure quality, maintain architectural integrity, and achieve knowledge transfer into ERCOT’s internal teams.
- Collaborate across Data Engineering, Architecture, Governance, Security, and business stakeholders to align AI/ML solutions with enterprise needs and regulatory responsibilities.
- Review and approve high‑risk deployments and exceptions, ensuring compensating controls are in place for ML and GenAI systems.
- Establish and track performance, reliability, and cost metrics for ML infrastructure, LLM usage, GenAI applications, and overall MLOps health.
- Communicate operational status, risks, and trade‑offs to executive stakeholders and technical partners with clarity and accountability.
Experience
- 8+ years in ML operations, MLOps engineering, AI/ML development, data engineering, or software engineering with ML/AI focus
- 2+ years leading ML operations teams or technical teams in ML/AI environments
- Demonstrated experience with enterprise-scale ML deployment, operations, and GenAI application development
ADDITIONAL QUALIFICATIONS
- Experience building and deploying ML/GenAI solutions using platforms like Azure ML, Azure AI, Databricks ML, and Azure OpenAI.
- Strong background in LLMs, RAG/semantic search, and AI agent or multi‑agent architectures.
- Proven MLOps expertise, including CI/CD for ML, model serving, monitoring, and production support.
- Leadership experience guiding technical teams and aligning engineering work with business and governance needs.
- Proficiency in Python and modern data/AI engineering practices, with familiarity in cloud infrastructure, vector databases, or AI observability tools.
Education
- Bachelor’s Degree: Computer Science, Data Science, or related filed (Required)
- Master’s Degree: Computer Science, Data Science, or related filed (Preferred)
- or a combination of education and experience that provides equivalent knowledge to a major in such fields is required
The foregoing description reflects the minimum qualifications and the essential functions of the position that must be performed proficiently with or without reasonable accommodation for individuals with disabilities. It is not an exhaustive list of the duties expected to be performed, and management may, at its discretion, revise or require that other or different tasks be performed as assigned. This job description is not intended to create a contract of employment with ERCOT. Both ERCOT and the employee may exercise their employment-at-will rights at any time. #LI-IV1
ERCOT is firmly committed to equal employment for all qualified persons without regard to race, sex, medical condition, religion, age, creed, national origin, citizenship status, marital status, sexual orientation, physical or mental disability, ancestry, veteran status, genetic information or any other protected category under federal, state or local law.
Expected Salary Range:
$145,295 - $247,001

INTRODUCTION
At ERCOT, our diverse and dynamic work environment provides a platform on which employees can work together to build the future of the Texas power grid and wholesale market utilizing the latest technologies and resources. We encourage you to join our talented, dedicated workforce to develop world-class solutions for today and tomorrow’s energy challenges while learning new skills and growing your career.
ERCOT is committed to fostering inclusion at all levels of our company. It is the cornerstone of our corporate values of accountability, leadership, innovation, trust, and expertise. We know that individuals with a wide variety of talents, ideas, and experiences propel the innovation that drives our success. An inclusive and diverse workforce strengthens us and allows for a collaborative environment to solve the challenges that face our industry today and in the future.
JOB SUMMARY
Leads the team responsible for developing, deploying, and operating machine learning models, generative AI applications, autonomous agents, and related AI solutions across ERCOT’s enterprise platforms. Oversees MLOps standards, production support, platform reliability, and governance for ML and GenAI assets. Balances delivery of new AI capabilities with operational excellence and ensures compliance with AI governance and model lifecycle controls. Partners closely with Data Operations, Data Engineering, Governance, Security, and business stakeholders to ensure safe, reliable, and efficient AI systems.
JOB DUTIES
- Responsible for hiring, coaching, training, and performance management of staff.
- Frequently interacts with reporting supervisors, customers, and/or functional peer group managers, normally involving matters between functional areas or customers.
- Responsible for the management of subordinate staff within a department. Typically has individual contributors as direct reports, but could have supervisory direct reports. Has full responsibility for direct reports.
- Generally provides input to budgeting and financial decisions that impact the department. Requests approval for financial actions beyond a limited scope.
ADDITIONAL JOB DUTIES
- Oversee end‑to‑end delivery of AI/ML and GenAI solutions, from design through deployment, ensuring enterprise‑ready quality, reliability, and security.
- Set technical direction and architectural standards for ML models, GenAI applications, autonomous agents, RAG systems, multimodal solutions, and vector/semantic search capabilities.
- Own and govern MLOps standards, including CI/CD automation, deployment pipelines, monitoring, evaluation frameworks, and model lifecycle controls for both ML and GenAI assets.
- Lead and develop the AI & ML Engineering team, including hiring, onboarding, coaching, performance management, and establishing clear skill ladders and growth pathways.
- Manage production ML/GenAI operations and Level 3 support, leading root‑cause investigations, incident command, post‑incident reviews, and long‑term problem management.
- Ensure compliance with ERCOT model governance and GenAI‑specific controls, including risk tiering, documentation, lineage, prompt management, safety guardrails, and regulatory requirements.
- Guide platform engineering for AI/ML infrastructure, including Azure ML, Databricks ML, vector databases, LLM orchestration frameworks, and ML/GenAI observability tooling.
- Plan and prioritize intake, releases, and roadmaps for ML and GenAI initiatives in partnership with Product Owners and Data Operations leadership.
- Oversee vendor and contractor contributions to ensure quality, maintain architectural integrity, and achieve knowledge transfer into ERCOT’s internal teams.
- Collaborate across Data Engineering, Architecture, Governance, Security, and business stakeholders to align AI/ML solutions with enterprise needs and regulatory responsibilities.
- Review and approve high‑risk deployments and exceptions, ensuring compensating controls are in place for ML and GenAI systems.
- Establish and track performance, reliability, and cost metrics for ML infrastructure, LLM usage, GenAI applications, and overall MLOps health.
- Communicate operational status, risks, and trade‑offs to executive stakeholders and technical partners with clarity and accountability.
Experience
- 8+ years in ML operations, MLOps engineering, AI/ML development, data engineering, or software engineering with ML/AI focus
- 2+ years leading ML operations teams or technical teams in ML/AI environments
- Demonstrated experience with enterprise-scale ML deployment, operations, and GenAI application development
ADDITIONAL QUALIFICATIONS
- Experience building and deploying ML/GenAI solutions using platforms like Azure ML, Azure AI, Databricks ML, and Azure OpenAI.
- Strong background in LLMs, RAG/semantic search, and AI agent or multi‑agent architectures.
- Proven MLOps expertise, including CI/CD for ML, model serving, monitoring, and production support.
- Leadership experience guiding technical teams and aligning engineering work with business and governance needs.
- Proficiency in Python and modern data/AI engineering practices, with familiarity in cloud infrastructure, vector databases, or AI observability tools.
Education
- Bachelor’s Degree: Computer Science, Data Science, or related filed (Required)
- Master’s Degree: Computer Science, Data Science, or related filed (Preferred)
- or a combination of education and experience that provides equivalent knowledge to a major in such fields is required
The foregoing description reflects the minimum qualifications and the essential functions of the position that must be performed proficiently with or without reasonable accommodation for individuals with disabilities. It is not an exhaustive list of the duties expected to be performed, and management may, at its discretion, revise or require that other or different tasks be performed as assigned. This job description is not intended to create a contract of employment with ERCOT. Both ERCOT and the employee may exercise their employment-at-will rights at any time. #LI-IV1
ERCOT is firmly committed to equal employment for all qualified persons without regard to race, sex, medical condition, religion, age, creed, national origin, citizenship status, marital status, sexual orientation, physical or mental disability, ancestry, veteran status, genetic information or any other protected category under federal, state or local law.
Expected Salary Range:
$145,295 - $247,001
AI ML Engineering Job Roles in Texas
See all 370+ AI ML Engineering Jobs in Texas
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Search AI ML Engineering Jobs in TexasAI ML Engineering Jobs in Texas: Frequently Asked Questions
Which companies sponsor visas for AI ML engineers in Texas?
Several large employers in Texas have established records of sponsoring work visas for AI and ML engineering roles. Dell Technologies in Round Rock, AT&T in Dallas, Texas Instruments, and Amazon Web Services operations in the state regularly file H-1B petitions for machine learning engineers and data scientists. Austin's tech corridor also includes companies like Apple, Google, and Tesla with significant AI hiring activity in the state.
Which visa types are most common for AI ML engineering roles in Texas?
The H-1B is the most common visa for AI and ML engineering positions in Texas, as these roles typically qualify as specialty occupations requiring at least a bachelor's degree in computer science, statistics, or a related field. Candidates with advanced degrees may also encounter EB-2 or EB-1B pathways through employer-sponsored green card petitions. Australians working in AI roles may qualify for the E-3 visa as an alternative to the H-1B.
Which cities in Texas have the most AI ML engineering sponsorship jobs?
Austin leads Texas for AI and ML engineering sponsorship activity, driven by its concentration of tech headquarters and satellite offices. Dallas and the broader DFW metroplex follow closely, with strong demand from finance, telecom, and enterprise software companies. Houston contributes through energy tech and healthcare AI, particularly around the Texas Medical Center, which has invested heavily in machine learning research and clinical applications.
How to find ai ml engineering visa sponsorship jobs in Texas?
Migrate Mate is built specifically for international job seekers and filters AI and ML engineering roles in Texas by visa sponsorship availability, saving you from applying to positions that won't support work authorization. The platform aggregates openings from employers with active H-1B and other sponsorship histories across Austin, Dallas, and Houston, making it easier to target companies already familiar with the sponsorship process for technical roles.
Are there any Texas-specific considerations for AI ML engineers seeking visa sponsorship?
Texas has no state income tax, which affects prevailing wage comparisons used in H-1B Labor Condition Applications since the DOL benchmarks wages against local market rates. Austin and Dallas metro areas have seen rapid salary growth in AI and ML roles, meaning DOL prevailing wage levels are updated regularly to reflect local conditions. Texas universities including UT Austin and Texas A&M also supply strong domestic and international graduate pipelines, making competition for sponsored roles particularly active.
What is the prevailing wage for sponsored ai ml engineering jobs in Texas?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.
See which ai ml engineering employers are hiring and sponsoring visas in Texas right now.
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