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Generative Ai Engineer Intern Jobs in Texas (NOW HIRING)

Generative AI Engineer

Houston, TX · On-site

$55 - $60/hr

Generative AI Engineer Work Location: Houston, TX Duration: 6-12 Months Contract Work Mode: Onsite from Day 1 Pay Rate Range: $50/hr. - $60/hr. on C2C Interview: Face to Face Interview is Mandatory ...

Senior Generative AI Engineer

Austin, TX · On-site

$54.75 - $70.50/hr

C. is seeking a Senior Generative AI Engineer to join their Apple Content Solutions Team. The role involves developing and maintaining AI-driven automation solutions while leveraging large language ...

Sr. Generative AI Developer

Dallas, TX · On-site

$120K - $161K/yr

Sr. Generative AI Developer Location: Dallas TX/ Tampa FL/ New Jersey - Hybrid Fulltime/FTE Salary: Market Client: Bank Role Overview We are seeking an experienced Senior Generative AI Developer to ...

Senior Generative AI Developer

Irving, TX · On-site

$116K - $157K/yr

We are seeking an experienced Senior Generative AI Developer to design and implement cutting-edge AI solutions leveraging Retrieval-Augmented Generation (RAG) techniques. The ideal candidate will ...

RAG,Python, Java, Agentic AI, LLMs, RAG, LangChain/LangGraph, Generative AI, AWS. We are seeking an experienced AI Engineer with strong expertise in Python, Java, Agentic AI, Large Language Models ...

Drive the roadmap for Generative AI use cases , ensuring scalability and real-world impact. Collaboration with Teams * Partner with transformation, engineering, and business teams to tailor AI agents ...

Generative AI Lead Engineer

Dallas, TX · On-site

$120 - $140/hr

About the role As a Generative AI Engineer , you will make an impact by designing and building advanced AI-powered applications and scalable solutions using modern technologies such as .NET, React ...

AI Engineer

Dallas, TX

$110K - $150K/yr

Experience with Generative AI Large Language Models (LLMs), including solution development and fine-tuning for domain-specific tasks. * Proficiency in at least one programming language such as Python ...

Showing results 21-40

Generative Ai Engineer Intern information

What does a generative AI engineer intern do?

A Generative AI Engineer Intern assists in developing and testing machine learning models, specifically those that can create new content such as text, images, or audio. They work with frameworks like TensorFlow or PyTorch, collaborate with senior engineers, and help improve the performance and reliability of generative AI systems. Interns may also be involved in data preprocessing, model evaluation, and keeping up with the latest research in artificial intelligence.

What skills and qualifications are needed to thrive as a generative AI engineer intern?

To thrive as a Generative AI Engineer Intern, you need a solid understanding of machine learning fundamentals, programming skills (especially in Python), and coursework or experience in artificial intelligence or computer science. Familiarity with deep learning frameworks like TensorFlow or PyTorch and version control systems such as Git is typically required, and relevant coursework or certifications in AI/ML are advantageous. Strong problem-solving skills, curiosity, and the ability to communicate complex ideas clearly help interns stand out. These skills and qualities are crucial for quickly learning advanced AI techniques, contributing to team projects, and driving innovation in a rapidly evolving field.

What types of projects can a generative AI engineer intern expect to work on during their internship?

As a Generative AI Engineer Intern, you can expect to work on projects involving the development, training, and evaluation of generative models such as GANs, VAEs, or transformer-based architectures. Typical tasks may include data preprocessing, model implementation, fine-tuning, and running experiments to improve model performance. Interns often collaborate closely with data scientists, software engineers, and research teams, gaining exposure to both research and application of AI in real-world products. This role provides hands-on experience with state-of-the-art tools and frameworks, offering a valuable foundation for a future career in AI engineering or research.

What is the difference between Generative Ai Engineer Intern vs Machine Learning Engineer Intern?

AspectGenerative Ai Engineer InternMachine Learning Engineer Intern
Required CredentialsBasic knowledge of AI, programming, and some coursework in machine learning or AIStrong foundation in machine learning, programming, and data analysis, often with coursework or certifications
Work EnvironmentTech companies, startups, research labs focusing on AI applicationsTech firms, research institutions, and companies applying machine learning models
Industry UsageDeveloping generative models like GPT, DALL·E, and similar AI toolsBuilding predictive models, data pipelines, and machine learning algorithms

While both roles involve AI and machine learning, a Generative Ai Engineer Intern focuses specifically on creating generative models like text, images, or audio, whereas a Machine Learning Engineer Intern works broadly on developing and deploying various machine learning algorithms across different applications.

What are the most commonly searched types of Generative Ai Engineer jobs in Texas? The most popular types of Generative Ai Engineer jobs in Texas are:
What cities in Texas are hiring for Generative Ai Engineer Intern jobs? Cities in Texas with the most Generative Ai Engineer Intern job openings:
Infographic showing various Generative Ai Engineer Intern job openings in Texas as of August 2026, with employment types broken down into 80% Full Time, 17% Part Time, and 3% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution.

Generative AI Engineer

Apetan Consulting llc

Houston, TX • On-site

$55 - $60/hr

Contractor

Posted 5 days ago


Job description

Title: Generative AI Engineer
Work Location: Houston, TX 
Duration: 6-12 Months Contract
Work Mode: Onsite from Day 1
Pay Rate Range: $50/hr. - $60/hr. on C2C
Interview: Face to Face Interview is Mandatory
 
If you've been building software for a few years, you've picked up real engineering instincts, and you're the person on your team who's already experimenting with agents and LLMs on the side — this is a chance to do that work for real, at enterprise scale, on production systems that matter.
 
What you will do:
Build agentic features, hands-on. Work alongside senior engineers to design and ship AI-powered remediation capabilities: agent workflows, retrieval pipelines, evaluation loops.
Write production code daily. Java/Spring Boot microservices and AI integrations on AWS.
Learn the full lifecycle. From an idea in a design doc to something running in production, instrumented, monitored, and improved based on real usage.
Bring fresh eyes. You're closer to the newest models, frameworks, and techniques than engineers who've been heads-down in one stack for a decade — we want that perspective in the room, not just deference to seniority.
Grow fast. Work directly with senior engineers who'll push your technical depth, and take on more ownership as you earn it.
 
Our stack
Java / Spring Boot microservices on AWS (Lambda, SQS, SNS, IAM, CloudWatch). 
AI Stack: AWS Bedrock and AgentCore, MCP for tool integration, RAG pipelines with vector and graph-based retrieval, LLM-as-judge evaluation. Some Python and Go at the edges. You won't know all of this — we're more interested in how fast you pick things up than what's already on your resume.
 
What you will bring:
4+ years building software professionally — you've shipped real features, not just coursework or side projects, and you understand what "production" demands.
Genuine curiosity about AI/agentic systems — you've built something with LLMs or agents, even outside of work: a side project, a hackathon entry, an experiment that didn't ship. We want to hear about it.
Solid engineering fundamentals — distributed systems basics, APIs, cloud infrastructure (AWS). You need to know what good code and good design look like.
You experiment and iterate ideas quickly and bring new perspectives 
 
Nice to have
Experience with multiple LLM APIs or agent frameworks
Python or Go 
POCs using AI for coding, automation, or data work 
Contributions to open source AI/ML tooling.