AI ML Engineering Jobs in Texas
AI ML Engineering jobs in Texas are among the most actively recruited technology roles in the state, concentrated in enterprise software, semiconductors, aerospace and defense, and financial technology, with openings at every level from junior data scientist to principal machine learning engineer. Austin, Dallas-Fort Worth, and Houston lead hiring volume, anchored by employers like Dell Technologies, Texas Instruments, and JPMorgan Chase, which maintain large engineering operations across the state. The most in-demand specialties are large language model development, computer vision, and MLOps infrastructure. Find a role that fits below and apply directly.
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Job Description
WHO WE ARE
Goldman Sachs is a leading global investment banking, securities and investment management firm that provides a wide range of services worldwide to a substantial and diversified client base that includes corporations, financial institutions, governments and high net-worth individuals. Founded in 1869, it is one of the oldest and largest investment banking firms. The firm is headquartered in New York and maintains offices in London, Bangalore, Frankfurt, Tokyo, Hong Kong and other major financial centres around the world. We are committed to growing our distinctive Culture and holding to our core values which always place our client's interests first. These values are reflected in our Business Principles, which emphasise integrity, commitment to excellence, innovation and teamwork.
Business Unit Overview
Enterprise Technology Operations (ETO) is a Business Unit within Core Engineering focused on running scalable production management services with a mandate of operational excellence and operational risk reduction achieved through large scale automation, best-in-class engineering, and application of data science and machine learning. The Production Runtime Experience (PRX) team in ETO applies software engineering and machine learning to production management services, processes, and activities to streamline monitoring, alerting, automation, and workflows.
TEAM OVERVIEW
The Machine Learning and Artificial Intelligence team in PRX applies advanced ML and GenAI to reduce the risk and cost of operating the firm’s large-scale compute infrastructure and extensive application estate. Building on strengths in statistical modelling, anomaly detection, predictive modelling, and time-series forecasting, we leverage foundational LLM Models to orchestrate multi-agent systems for automated production management services. By unifying classical ML with agentic AI, we deliver reliable, explainable, and cost-efficient operations at scale.
ROLE AND RESPONSIBILITIES
In this role, you will be responsible for launching and implementing GenAI agentic solutions aimed at reducing the risk and cost of managing large-scale production environments with varying complexities. You will address various production runtime challenges by developing agentic AI solutions that can diagnose, reason, and take actions in production environments to improve productivity and address issues related to production support.
What You’ll Do:
- Build agentic AI systems: Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following MCP protocol. Engineer robust guardrails for safety, compliance, and least-privilege access.
- Productionize LLMs: Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations.
- Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability.
- Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost; and deliver auditable, business-aligned outcomes.
- Safety, reliability, and governance: Build validator models, adversarial prompts, and policy checks into the stack; enforce deterministic fallbacks, circuit breakers, and rollback strategies; instrument continuous evaluations for usefulness, correctness, and risk.
- Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool-calls to meet stringent SLOs under real-world load.
- Build a RAG pipeline: Curate domain-knowledge; build data-quality validation framework; establish feedback loops and milestone framework maintain knowledge freshness.
- Raise the bar: Drive design reviews, experiment rigor, and high-quality engineering practices; mentor peers on agent architectures, evaluation methodologies, and safe deployment patterns.
Qualifications
A Bachelor’s degree (Masters/ PhD preferred) in a computational field (Computer Science, Applied Mathematics, Engineering, or in a related quantitative discipline), with 5+ years of experience as an applied data scientist / machine learning engineer.
Essential Skills
- 5+ years of software development in one or more languages (Python, C/C++, Go, Java); strong hands-on experience building and maintaining large-scale Python applications preferred.
- 3+ years designing, architecting, testing, and launching production ML systems, including model deployment/serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows.
- Practical experience with Large Language Models (LLMs): API integration, prompt engineering, finetuning/adaptation, and building applications using RAG and tool-using agents (vector retrieval, function calling, secure tool execution).
- Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude).
- Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver efficient and reliable solutions.
- Strong analytical problem-solving, ownership, and urgency; ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact.
- Preferred: Proficiency building and operating on cloud infrastructure (ideally AWS), including containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift), orchestration (Step Functions), model serving (SageMaker), and infra-as-code (Terraform/CloudFormation).
YOUR CAREER
Goldman Sachs is a meritocracy where you will be given all the tools to advance your career. At Goldman Sachs, you will have access to excellent training programs designed to improve multiple facets of your skill portfolio. Our in-house training program, “Goldman Sachs University” offers a comprehensive series of courses that you will have access to as your career progresses. Goldman Sachs University has an impressive catalogue of courses which span technical, business and leadership skills.
Same Posting Description for Internal and External Candidates
Job Description
WHO WE ARE
Goldman Sachs is a leading global investment banking, securities and investment management firm that provides a wide range of services worldwide to a substantial and diversified client base that includes corporations, financial institutions, governments and high net-worth individuals. Founded in 1869, it is one of the oldest and largest investment banking firms. The firm is headquartered in New York and maintains offices in London, Bangalore, Frankfurt, Tokyo, Hong Kong and other major financial centres around the world. We are committed to growing our distinctive Culture and holding to our core values which always place our client's interests first. These values are reflected in our Business Principles, which emphasise integrity, commitment to excellence, innovation and teamwork.
Business Unit Overview
Enterprise Technology Operations (ETO) is a Business Unit within Core Engineering focused on running scalable production management services with a mandate of operational excellence and operational risk reduction achieved through large scale automation, best-in-class engineering, and application of data science and machine learning. The Production Runtime Experience (PRX) team in ETO applies software engineering and machine learning to production management services, processes, and activities to streamline monitoring, alerting, automation, and workflows.
TEAM OVERVIEW
The Machine Learning and Artificial Intelligence team in PRX applies advanced ML and GenAI to reduce the risk and cost of operating the firm’s large-scale compute infrastructure and extensive application estate. Building on strengths in statistical modelling, anomaly detection, predictive modelling, and time-series forecasting, we leverage foundational LLM Models to orchestrate multi-agent systems for automated production management services. By unifying classical ML with agentic AI, we deliver reliable, explainable, and cost-efficient operations at scale.
ROLE AND RESPONSIBILITIES
In this role, you will be responsible for launching and implementing GenAI agentic solutions aimed at reducing the risk and cost of managing large-scale production environments with varying complexities. You will address various production runtime challenges by developing agentic AI solutions that can diagnose, reason, and take actions in production environments to improve productivity and address issues related to production support.
What You’ll Do:
- Build agentic AI systems: Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following MCP protocol. Engineer robust guardrails for safety, compliance, and least-privilege access.
- Productionize LLMs: Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations.
- Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability.
- Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost; and deliver auditable, business-aligned outcomes.
- Safety, reliability, and governance: Build validator models, adversarial prompts, and policy checks into the stack; enforce deterministic fallbacks, circuit breakers, and rollback strategies; instrument continuous evaluations for usefulness, correctness, and risk.
- Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool-calls to meet stringent SLOs under real-world load.
- Build a RAG pipeline: Curate domain-knowledge; build data-quality validation framework; establish feedback loops and milestone framework maintain knowledge freshness.
- Raise the bar: Drive design reviews, experiment rigor, and high-quality engineering practices; mentor peers on agent architectures, evaluation methodologies, and safe deployment patterns.
Qualifications
A Bachelor’s degree (Masters/ PhD preferred) in a computational field (Computer Science, Applied Mathematics, Engineering, or in a related quantitative discipline), with 5+ years of experience as an applied data scientist / machine learning engineer.
Essential Skills
- 5+ years of software development in one or more languages (Python, C/C++, Go, Java); strong hands-on experience building and maintaining large-scale Python applications preferred.
- 3+ years designing, architecting, testing, and launching production ML systems, including model deployment/serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows.
- Practical experience with Large Language Models (LLMs): API integration, prompt engineering, finetuning/adaptation, and building applications using RAG and tool-using agents (vector retrieval, function calling, secure tool execution).
- Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude).
- Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver efficient and reliable solutions.
- Strong analytical problem-solving, ownership, and urgency; ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact.
- Preferred: Proficiency building and operating on cloud infrastructure (ideally AWS), including containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift), orchestration (Step Functions), model serving (SageMaker), and infra-as-code (Terraform/CloudFormation).
YOUR CAREER
Goldman Sachs is a meritocracy where you will be given all the tools to advance your career. At Goldman Sachs, you will have access to excellent training programs designed to improve multiple facets of your skill portfolio. Our in-house training program, “Goldman Sachs University” offers a comprehensive series of courses that you will have access to as your career progresses. Goldman Sachs University has an impressive catalogue of courses which span technical, business and leadership skills.
Same Posting Description for Internal and External Candidates
See All 278+ AI ML Engineering Jobs in Texas
Find roles in Texas that match your experience and apply in just a few clicks.
Find AI ML Engineering JobsAI ML Engineering Jobs by City in Texas
Where Texas roles are concentrated, by current openings.
AI ML Engineering 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 engineering jobs across Texas.
- Bachelor's or master's degree in computer science, mathematics, or a related engineering field
- Proficiency in Python and machine learning frameworks such as TensorFlow or PyTorch
- Hands-on experience designing, training, and deploying production-grade ML models
- Familiarity with cloud platforms including AWS, Google Cloud, or Microsoft Azure
- Experience with MLOps tooling, model monitoring, and CI/CD pipelines for ML systems
- Strong foundation in statistics, linear algebra, and applied optimization methods
AI ML Engineering Jobs in Texas: Frequently Asked Questions
How do you become a ai ml engineering in Texas?
The most direct path is a bachelor's degree in computer science, electrical engineering, or applied mathematics, followed by hands-on ML project work that builds a demonstrable portfolio. Texas has no state-issued license specific to AI or machine learning engineering. Most Texas employers, particularly in Austin's tech corridor and Dallas-Fort Worth's enterprise sector, screen candidates on portfolio strength, relevant internships, and proficiency with production ML tools rather than a formal certification.
Which companies hire ai ml engineerings in Texas?
Employers hiring ai ml engineerings 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 semiconductor manufacturers, defense contractors, and financial institutions means demand extends well beyond pure software companies.
Which Texas cities have the most ai ml engineering jobs?
Austin, Dallas, and Irving account for the largest share of ai ml engineering openings in Texas. Austin's dense startup and big-tech ecosystem drives a large portion of listings, while Dallas-Fort Worth's enterprise technology and financial services base sustains steady corporate demand, and Houston's energy and aerospace industries generate specialized ML roles tied to those sectors.
Are there remote ai ml engineering jobs in Texas?
Yes, and more than most fields. About 16% of ai ml engineering openings tied to Texas are remote or hybrid as of June 2026, reflecting the desk-based and infrastructure-heavy nature of the work. Model training, data pipeline engineering, and research roles tend to be the most remote-compatible, while positions requiring access to proprietary hardware or classified defense systems are more likely to require on-site presence.
How can I get hired as a ai ml engineering in Texas with little or no experience?
The most realistic entry path is applying to data analyst or software engineer roles at large Texas employers that offer internal ML rotation programs, then transitioning once you have production exposure. Companies in Austin and Dallas-Fort Worth with established engineering organizations regularly bring on new graduates through structured associate engineer programs. Building a public portfolio on GitHub with end-to-end ML projects and earning a recognized cloud ML certification gives candidates a concrete edge over applicants without formal work history.
Where can I find and apply to ai ml engineering jobs in Texas?
You can find and apply to ai ml engineering jobs in Texas on Migrate Mate, which lists current Texas openings across Austin, Dallas-Fort Worth, Houston, and other markets. Find roles that fit your experience and specialization and apply directly to the employers posting them.
See All 278+ AI ML Engineering Jobs in Texas
Find roles in Texas that match your experience and apply in just a few clicks.
Find AI ML Engineering Jobs