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Generative Ai Engineer Jobs in Austin, TX (NOW HIRING)

MDAEdge

Generative AI Engineer

Austin, TX · On-site

MDAEdge is a company focusing on innovative AI solutions, and they are seeking a Generative AI Engineer. The role involves developing efficient pipelines for document processing and creating scalable ...

Northern Base

Gen AI Engineer

Austin, TX · On-site

Generative AI & Large Language Models (LLMs) * Strong Java & Python Development * AI-Driven ... Prompt Engineering & LLM Orchestration * LLM Evaluation Frameworks & Responsible AI Practices

Deloitte

Lead Generative AI Data Engineer III

Austin, TX · On-site

$101K - $133K/yr

Share this job: Share: Share Lead Generative AI Data Engineer III with Facebook Share Lead Generative AI Data Engineer III with LinkedIn Share Lead Generative AI Data Engineer III with Twitter ...

Addison Group

Senior Full Stack AI Engineer

Austin, TX · Remote

$85 - $95/hr

Design, build, and support AI-powered applications using LLMs, generative AI platforms, and agentic ... Establish engineering standards, conduct code reviews, and mentor team members. * Lead AI model ...

Adidev Technologies Inc

ML/AI Engineers

Austin, TX

Adidev is looking for an adept Machine Learning Engineer to take the helm in deploying advanced machine learning models, with a special emphasis on Generative AI. In this role, you will craft and ...

Adidev Technologies Inc

ML/AI Engineers

Austin, TX · On-site

Adidev is looking for an adept Machine Learning Engineer to take the helm in deploying advanced machine learning models, with a special emphasis on Generative AI. In this role, you will craft and ...

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Generative Ai Engineer information

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$37.7K

$114.8K

$189.8K

How much do generative ai engineer jobs pay per year?

As of Aug 27, 2026, the average yearly pay for generative ai engineer in Austin, TX is $114,819.00, according to ZipRecruiter salary data. Most workers in this role earn between $82,200.00 and $150,100.00 per year, depending on experience, location, and employer.

What is a generative AI engineer?

A Generative AI Engineer is a specialized software engineer who designs, develops, and optimizes AI models that generate content such as text, images, audio, or video. They work with deep learning frameworks, train large-scale models, and fine-tune pre-trained architectures to improve performance. Their role involves data preprocessing, model deployment, and continuous optimization to enhance AI-generated outputs. Generative AI Engineers typically collaborate with data scientists, researchers, and product teams to integrate AI solutions into applications and services.

What does a generative AI engineer do?

As a Generative AI Engineer, your typical responsibilities involve designing, developing, and optimizing generative models for tasks such as image synthesis, natural language generation, or data augmentation. You will often collaborate closely with data scientists, researchers, and product teams to translate business or research goals into scalable AI solutions. Day-to-day work may include experimenting with different neural network architectures, optimizing model performance, and deploying models to production environments. Many roles also offer opportunities to contribute to publications or open-source projects, and there is strong potential for career growth into lead engineering or research positions as you gain experience.

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

To thrive as a Generative AI Engineer, you need a deep understanding of machine learning, deep learning architectures (such as GANs and transformers), and proficiency in programming languages like Python, along with a degree in computer science, engineering, or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, and relevant cloud platforms, as well as certifications in AI or ML, are highly valuable. Strong problem-solving skills, creativity, and effective communication help engineers collaborate and innovate within diverse, multidisciplinary teams. These skills are critical for developing advanced AI models, driving continuous improvement, and successfully translating complex research into practical applications.

How do I become a generative AI engineer?

To become a generative AI engineer, you should have a strong foundation in programming languages such as Python, experience with machine learning frameworks like TensorFlow or PyTorch, and knowledge of deep learning models such as GANs or transformers. Gaining expertise through relevant coursework, online tutorials, and hands-on projects is essential, along with understanding data preprocessing and model evaluation. Building a portfolio of AI projects and staying updated with the latest research can also improve job prospects in this field.

What is the salary of a generative AI engineer?

The salary of a generative AI engineer typically ranges from $100,000 to $180,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in deep learning and machine learning frameworks may earn higher compensation, often including bonuses and stock options.

What are the most commonly searched types of Generative Ai Engineer jobs in Austin, TX?

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For Generative Ai Engineer jobs in Austin, TX, the most frequently searched job titles are:

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Generative Ai Engineer jobs near you
Infographic showing various Generative Ai Engineer job openings in Austin, TX as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $114,819 per year, or $55.2 per hour.

Generative AI Engineer

Austin, TX • On-site

MDAEdge
Custom Software Development Services • 51 - 200 employees
Learn more about MDAEdgeexternal

Full-time

Re-posted 6 days ago

Job description

Job Summary:
MDAEdge is a company focusing on innovative AI solutions, and they are seeking a Generative AI Engineer. The role involves developing efficient pipelines for document processing and creating scalable AI applications while collaborating with architects and developers.
Responsibilities:
• Develop pipelines to parse documents, chunk, vectorise, and store vector data.
• Design schemas for vector stores to optimise retrieval efficiency.
• Create prompt templates using advanced prompt engineering techniques.
• Implement few-shot prompting strategies for improved LLM interactions.
• Develop semantic agents and interfaces for seamless agent collaboration.
• Build microservices and expose them as APIs for scalable solutions.
• Work closely with architects and full-stack developers to deliver AI solutions.
• Apply best practices for data management and model deployment.
• Ensure scalability and performance of generative AI applications.
Qualifications:
Required:
• Designed and developed scalable generative AI platforms for enterprise use cases.
• Built end-to-end pipelines for document parsing, chunking, vectorisation, and storage in vector databases like FAISS, Pinecone, and Chroma.
• Created and optimised vector store schemas for efficient semantic retrieval.
• Developed prompt templates using few-shot and zero-shot prompting techniques.
• Engineered semantic agents and built interfaces for agent collaboration in LLM workflows.
• Built and deployed microservices exposing LLM capabilities through scalable APIs.
Company:
The world doesn't have a talent shortage. It has a talent alignment problem. MDA Edge exists to fix that. Founded in , the company is headquartered in Sheridan, WY, US, , with a team of 51-200 employees. The company is currently Growth Stage.

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