AI ML Engineer Jobs in Texas
AI ML Engineer jobs in Texas are among the most active in the country, with strong demand concentrated in tech, financial services, energy, defense, and semiconductor manufacturing and openings that range from entry-level data science associates through principal engineers and applied research leads. Austin, Dallas, and Houston are the primary hiring centers, where companies like Texas Instruments, ExxonMobil, and Dell Technologies maintain significant AI and machine learning teams. The most in-demand specialties include large language model (LLM) development, computer vision, MLOps, and predictive analytics applied to energy and financial data. Find a role that fits below and apply directly.
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Showing 5 of 278+ AI ML Engineer jobs
Senior Lead AI/ML Engineer - R01567102
About Brillio:
Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Brillio, renowned for its world-class professionals, referred to as "Brillians", distinguishes itself through their capacity to seamlessly integrate cutting-edge digital and design thinking skills with an unwavering dedication to client satisfaction. Brillio takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. Brillio's relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work® certification year after year.
Primary Skills
- Value Quantification: Pre-Model Development, Model Provisioning: Kubernetes, Kibana, Model Monitoring, Cloud Computing, Python/PySpark, SAS/SPSS, Great Expectation, Evidently AI, Deployment Strategies (A/B, Blue green, Canary), Model testing, Tools (KubeFlow, BentoML), Integration testing, ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Value Quantification: Post-Model Deployment, Model Experimentation, R/ R Studio
Specialization
- ML Engineering: AI/ML Engineer
Job Requirements
Senior Lead AI/Full Stack Engineer
Location: Deerfield, FL
Work Type: Hybrid Model with min 2-3 days per week
The mission
Brillio is standing up this team because AI has collapsed attacker timelines from weeks to hours. You’ll build the AI-native tooling that lets a small senior pod respond at machine speed, implementing work-focused security agents and the deterministic orchestration around them, wired into vulnerability-response stack (SIEM/SOAR/EDR, identity, vuln management, CMDB, ITSM). This is a hands-on-keyboard seat: you write production code, run agent fleets, and ship across the full SDLC.
What you’ll actually do
- Write production code. Repeatable work becomes compiled, testable binaries (Go-first); prompts drive persona behavior only. You build the tooling, not slideware about it.
- Operate agent fleets. Take a decomposed backlog, fan out parallel streams on conflict-free work, and bring it back through review gates.
- Build security in, not on. TDD test-writers ahead of coders, a dedicated validation stage (models hallucinate passing tests), persona judging against threat/security models, failures auto-looped as issues. Signed builds, SBOMs, and access control are table stakes.
- Integrate natively. Azure + GitHub Copilot, build-what’s-missing in-stack. No third-party platforms, no AWS.
- Run model-agnostic. Personas across Claude / Copilot / ChatGPT because they fail differently; disagreement is signal. Manage token and context economics as an engineering constraint — just-in-time context loading, per-task budgets, alerts.
- Own your slice end to end. Design, develop, test, integrate, deploy, document, and KT — across the full SDLC.
Must-have
- Senior engineer who still ships daily — hands-on-keyboard delivery, no exceptions.
- Deep Azure + GitHub Copilot delivery experience (primary stack, not a footnote).
- Strong Go (or equivalent systems language) for deterministic, testable orchestration tooling.
- Hands-on experience operating multi-agent / multi-model workflows in real delivery, not demos.
- Security-native instincts — TDD discipline, secure SDLC, threat modeling; comfort in a vulnerability-response context.
- Full-SDLC ownership of a work stream: design through deployment, docs, and knowledge transfer.
Nice-to-have
- SIEM / SOAR / EDR, identity, vuln-management, or CMDB / ITSM integration experience.
- Eval-harness, red-team, or agent-guardrail work (blast-radius sandboxing, reversibility, confidence thresholds, audit trails).
Salary: 140-150 USD per year salary
Senior Lead AI/ML Engineer - R01567102
About Brillio:
Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Brillio, renowned for its world-class professionals, referred to as "Brillians", distinguishes itself through their capacity to seamlessly integrate cutting-edge digital and design thinking skills with an unwavering dedication to client satisfaction. Brillio takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. Brillio's relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work® certification year after year.
Primary Skills
- Value Quantification: Pre-Model Development, Model Provisioning: Kubernetes, Kibana, Model Monitoring, Cloud Computing, Python/PySpark, SAS/SPSS, Great Expectation, Evidently AI, Deployment Strategies (A/B, Blue green, Canary), Model testing, Tools (KubeFlow, BentoML), Integration testing, ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Value Quantification: Post-Model Deployment, Model Experimentation, R/ R Studio
Specialization
- ML Engineering: AI/ML Engineer
Job Requirements
Senior Lead AI/Full Stack Engineer
Location: Deerfield, FL
Work Type: Hybrid Model with min 2-3 days per week
The mission
Brillio is standing up this team because AI has collapsed attacker timelines from weeks to hours. You’ll build the AI-native tooling that lets a small senior pod respond at machine speed, implementing work-focused security agents and the deterministic orchestration around them, wired into vulnerability-response stack (SIEM/SOAR/EDR, identity, vuln management, CMDB, ITSM). This is a hands-on-keyboard seat: you write production code, run agent fleets, and ship across the full SDLC.
What you’ll actually do
- Write production code. Repeatable work becomes compiled, testable binaries (Go-first); prompts drive persona behavior only. You build the tooling, not slideware about it.
- Operate agent fleets. Take a decomposed backlog, fan out parallel streams on conflict-free work, and bring it back through review gates.
- Build security in, not on. TDD test-writers ahead of coders, a dedicated validation stage (models hallucinate passing tests), persona judging against threat/security models, failures auto-looped as issues. Signed builds, SBOMs, and access control are table stakes.
- Integrate natively. Azure + GitHub Copilot, build-what’s-missing in-stack. No third-party platforms, no AWS.
- Run model-agnostic. Personas across Claude / Copilot / ChatGPT because they fail differently; disagreement is signal. Manage token and context economics as an engineering constraint — just-in-time context loading, per-task budgets, alerts.
- Own your slice end to end. Design, develop, test, integrate, deploy, document, and KT — across the full SDLC.
Must-have
- Senior engineer who still ships daily — hands-on-keyboard delivery, no exceptions.
- Deep Azure + GitHub Copilot delivery experience (primary stack, not a footnote).
- Strong Go (or equivalent systems language) for deterministic, testable orchestration tooling.
- Hands-on experience operating multi-agent / multi-model workflows in real delivery, not demos.
- Security-native instincts — TDD discipline, secure SDLC, threat modeling; comfort in a vulnerability-response context.
- Full-SDLC ownership of a work stream: design through deployment, docs, and knowledge transfer.
Nice-to-have
- SIEM / SOAR / EDR, identity, vuln-management, or CMDB / ITSM integration experience.
- Eval-harness, red-team, or agent-guardrail work (blast-radius sandboxing, reversibility, confidence thresholds, audit trails).
Salary: 140-150 USD per year salary
See All 278+ AI ML Engineer Jobs in Texas
Find roles in Texas that match your experience and apply in just a few clicks.
Find AI ML Engineer JobsAI ML Engineer Jobs by City in Texas
Where Texas roles are concentrated, by current openings.
AI ML Engineer Job Market in Texas
A snapshot from current Texas openings, updated as new roles post.
Who's Hiring
- Apple57
- LTIMindtree14
- Tiger Analytics11
- PepsiCo7
- Photon7
Top Industries Hiring
- Technology & Software100
- Electronics & Hardware60
- Consulting & Professional Services36
- Banking & Financial Services30
- Energy14
What Texas Employers Look For
The qualifications that appear most often in AI ML engineer jobs across Texas.
- Bachelor's or master's degree in computer science, statistics, mathematics, or a related field
- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn
- Experience designing, training, and deploying machine learning models in production environments
- Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud for model hosting
- Strong knowledge of data pipelines, feature engineering, and experiment tracking tools like MLflow
- Ability to communicate model performance and limitations clearly to non-technical stakeholders
AI ML Engineer Jobs in Texas: Frequently Asked Questions
How do you become a ai ml engineer in Texas?
There is no state-issued license required to work as an ai ml engineer in Texas, so the path runs through education and demonstrated technical skill. Most roles require at least a bachelor's degree in computer science, data science, mathematics, or a closely related field, with a master's degree increasingly preferred for senior positions. Texas employers place heavy weight on a portfolio of real projects, competition experience such as Kaggle, and internships completed at Texas-based tech, energy, or semiconductor companies.
Which companies hire ai ml engineers in Texas?
Employers hiring ai ml engineers in Texas right now include Apple, LTIMindtree, and Tiger Analytics, based on current listings on Migrate Mate as of June 2026. Texas's concentration of energy, defense, and semiconductor companies means demand extends well beyond traditional tech firms into sectors where applied ML is reshaping core operations.
Which Texas cities have the most ai ml engineer jobs?
Austin, Dallas, and Irving account for the largest share of ai ml engineer openings in Texas. Austin's dense startup and semiconductor ecosystem drives its volume, Dallas attracts financial services and telecom firms with large AI teams, and Houston's energy sector has invested heavily in predictive modeling and optimization roles tied to oil and gas operations.
Are there remote ai ml engineer jobs in Texas?
Yes, and more than most fields. About 16% of ai ml engineer openings tied to Texas are remote or hybrid as of June 2026, reflecting that most of the work involves writing code, training models, and analyzing data rather than being on-site. Fully remote roles are most common in research-oriented positions and at software-first companies, while roles involving proprietary hardware, defense systems, or on-premises data centers typically require in-person work.
How can I get hired as a ai ml engineer in Texas with little or no experience?
The most realistic entry path is through an associate or junior data scientist role at a mid-size Texas tech or energy company, where the scope is narrow enough for new graduates to contribute quickly. Texas Instruments and Dell Technologies run structured early-career programs that include applied ML rotations. Building a public GitHub portfolio with end-to-end projects and completing a recognized credential such as the AWS Certified Machine Learning Specialty or Google Professional ML Engineer certificate gives candidates from adjacent roles like data analyst or software developer a concrete edge when applying.
Where can I find and apply to ai ml engineer jobs in Texas?
You can find and apply to ai ml engineer jobs in Texas on Migrate Mate, which lists current Texas openings across Austin, Dallas, Houston, and other markets statewide. Search the listings for roles that match your experience level and specialty, then apply directly to the ones that fit.
See All 278+ AI ML Engineer Jobs in Texas
Find roles in Texas that match your experience and apply in just a few clicks.
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