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Ai Rag Jobs in Austin, TX (NOW HIRING)

Vestwell

Manager, Software Engineer (AI)

Austin, TX · On-site

Guide the implementation of modern AI capabilities, including retrieval-augmented generation (RAG), AI agents, orchestration frameworks, and integrations with leading LLM providers. * Establish ...

LPL Financial

AVP, Principal AI Engineer

Austin, TX

Architect and build AI-powered applications using modern technologies such as LLMs, generative AI, agentic workflows, RAG architectures, and machine learning models. * Lead the end-to-end software ...

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Future Secure AI

AI Engineer, Data Science

Austin, TX · On-site

$113K - $136K/yr

Commercial experience with modern LLM ecosystems (e.g., LangChain, LlamaIndex, RAG pipelines, multi ... Future Secure AI Privacy Policy At Future Secure AI, we are committed to protecting your privacy ...

AgileEngine

AI Platform Tech Lead ID84742

Austin, TX · On-site

You will design and build AI agents, skills, and RAG integrations using MCP, function calling, and APIs, implement secure integrations across enterprise systems, and establish technical standards for ...

Elara Caring

AI Automation Engineer II

Austin, TX · On-site

Building or contributing to agentic workflows, retrieval-augmented generation (RAG), and LLM ... Working knowledge of Python for AI workflows, data processing, and integrations (preferred)

Bigcommerce

Lead Software Engineer - AI

Austin, TX

Develop LLM-based solutions, including classification, extraction, structured generation, and RAG ... Mentor Senior AI Engineers through design reviews and technical guidance * Collaborate with Staff ...

BigCommerce Pty.

Lead Software Engineer - AI

Austin, TX · On-site

Develop LLM-based solutions, including classification, extraction, structured generation, and RAG ... Mentor Senior AI Engineers through design reviews and technical guidance* Collaborate with Staff ...

Commerce.com

Lead Software Engineer - AI

Austin, TX · On-site

Develop LLM-based solutions, including classification, extraction, structured generation, and RAG ... Mentor Senior AI Engineers through design reviews and technical guidance * Collaborate with Staff ...

BigCommerce Pty

Lead Software Engineer - AI

Austin, TX · On-site

Develop LLM-based solutions, including classification, extraction, structured generation, and RAG ... Mentor Senior AI Engineers through design reviews and technical guidance* Collaborate with Staff ...

Feedonomics

Lead Software Engineer - AI

Austin, TX · On-site +1

Develop LLM-based solutions, including classification, extraction, structured generation, and RAG ... Mentor Senior AI Engineers through design reviews and technical guidance * Collaborate with Staff ...

Efinity Mortgage

Ai Solutions Engineer

Austin, TX · On-site

... RAG patterns, prompt and workflow design, and AI tool orchestration. • Experience integrating with existing business systems (CRMs, document platforms, internal databases, reporting tools). • A ...

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All JobsAi Rag JobsAi Rag Jobs in Austin, TX

Ai Rag information

See Austin, TX salary details

$31.7K

$57.7K

$82.8K

How much do ai rag jobs pay per year?

As of Sep 12, 2026, the average yearly pay for ai rag in Austin, TX is $57,733.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,600.00 and $64,400.00 per year, depending on experience, location, and employer.

What is an AI RAG?

AI RAGs, or Retrieval-Augmented Generation systems, are a type of artificial intelligence that combines the power of retrieving information from large databases or documents with generating human-like text responses. This approach allows AI models to provide more accurate, up-to-date, and contextually relevant answers by referencing external data sources during the generation process. RAGs are commonly used in applications like chatbots, search engines, and customer support systems, where comprehensive and factual responses are important.

What are the key skills and qualifications needed to thrive as an AI researcher?

To thrive as an AI Researcher, you need a strong background in computer science, mathematics, and machine learning, usually with an advanced degree such as a Master's or Ph.D. Proficiency with programming languages like Python, deep learning frameworks (e.g., TensorFlow, PyTorch), and familiarity with scientific research tools is essential. Critical thinking, creativity, and effective collaboration are vital soft skills for generating novel ideas and working in multidisciplinary teams. These skills and qualities are crucial to drive innovation and solve complex problems in the rapidly evolving field of artificial intelligence.

What are common challenges faced by AI RAG engineers when integrating retrieval systems with large language models?

AI RAG engineers often encounter challenges such as ensuring seamless integration between retrieval systems and language models, maintaining low latency for real-time responses, and handling the quality and relevance of retrieved data. Additionally, tuning the system to balance retrieval accuracy with generative fluency can be complex, especially when dealing with large or unstructured datasets. Collaboration with data engineers, ML researchers, and product teams is essential to address these challenges and optimize system performance.

What is the difference between Ai Rag vs Data Analyst?

AspectAi RagData Analyst
Required CredentialsTypically a diploma or certification in AI, machine learning, or related fieldsBachelor's degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in AI development and researchAcross industries for data interpretation and decision-making
Common Search & ComparisonYesYes

Ai Rag and Data Analyst roles share overlapping skills in data handling and analysis, but Ai Rag focuses more on AI-specific applications and machine learning, while Data Analysts concentrate on interpreting data to inform business decisions. Both roles are vital in data-driven industries, with Ai Rag often working in AI development environments and Data Analysts supporting strategic insights across sectors.

What are popular job titles related to Ai Rag jobs in Austin, TX?

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

What job categories do people searching Ai Rag jobs in Austin, TX look for?

The top searched job categories for Ai Rag jobs in Austin, TX are:

What cities near Austin, TX are hiring for Ai Rag jobs?

Cities near Austin, TX with the most Ai Rag job openings:

Ai Rag jobs near you
Infographic showing various Ai Rag job openings in Austin, TX as of August 2026, with employment types broken down into 78% Full Time, 19% Part Time, and 3% Contract. Highlights an 69% Physical, 4% Hybrid, and 27% Remote job distribution, with an average salary of $57,733 per year, or $27.8 per hour.

Senior Product Manager- AI & Agentic

Austin, TX • On-site

Future Secure AI
See more jobs at Future Secure AIexternal

$125K - $165K/yr

Full-time

Re-posted 25 days ago

Job description

About the Role 

Future Secure AI is looking for a Senior Product Manager to own the AI layer of our enterprise agentic AI platform. You will be responsible for the evaluation of capability, trust and governance posture, observability and explainability primitives, and the agent-design patterns that engineering teams use to build AI Co-Workers for enterprise customers. Your goal is to make the AI layer reliable, auditable, and adoptable in regulated environments, so every AI Co-Worker built on the platform inherits enterprise-grade quality. You will operate as a peer of engineering and AI/ML leadership, with direct impact on whether AI Co-Workers reach scale in regulated industries. 

Responsibilities 

  • Own the platform's evaluation capability and the quality bar that every AI Co-Worker must meet before it ships, spanning offline, online, regression, and rubric-based dimensions 
  • Advance the platform's AI governance posture covering trust, explainability, observability, and audit to meet the bar set by enterprise compliance reviews across regulated industries 
  • Own the platform's agent-design primitives (planning, tool use, memory, retries, circuit breakers, human-in-loop checkpoints) and the patterns that guide engineering teams to use them well 
  • Own the model and prompt lifecycle: how versions are introduced, evaluated, promoted, and retired, with change management appropriate to enterprise customers 
  • Drive discovery through interviews with platform engineers, production behavior analysis, frontier research, and enterprise customer conversations, and prototype eval and guardrail primitives before committing engineering capacity 
  • Partner closely with engineering leadership to ensure the platform delivers a reliable, intuitive experience for teams building agentic AI Co-Workers 
  • Own value risk and viability risk: ensure engineering teams adopt your primitives as the fastest path to a reliable AI Co-Worker, and that governance, observability, and audit features satisfy enterprise compliance requirements 

Minimum Qualifications 

  • Demonstrated experience building or owning an evaluation suite for an AI feature in production, including offline benchmarks, online evals, regression evals, and rubric-based grading 
  • Nuanced understanding of foundation models, agent architectures, and AI evaluation, with the ability to simplify that complexity for executive, business, and regulated-customer audiences 
  • Direct experience with non-determinism in AI systems; able to write acceptance criteria that hold up under flaky model behavior and set quality SLAs that do not break the team 
  • Experience shipping or working closely on a production agent system, with informed opinions on planning loops, tool use, memory, retries, guardrails, and when an agent is the wrong abstraction 
  • Familiarity with NIST AI RMF and the EU AI Act, and the ability to map enterprise compliance requirements across banking, insurance, and healthcare to platform primitives 
  • Experience living through an AI incident, root-causing it, and shipping the fix into the platform so downstream teams inherit the improvement 

Preferred Qualifications 

  • Background as an AI or ML PM at an enterprise AI platform, an AI tooling vendor, or a model lab 
  • Experience in a product role at a regulated-industry AI team that has shipped to production 
  • Familiarity with RAG patterns, eval tooling, agent frameworks, and the broader AI tools landscape 
  • ML-leaning PM background at a company that has matured past the prototype phase 

Why Join Us? 

  • A high-performance culture 
  • State-of-the-art technology 
  • Experience world-class leadership 
  • Scale of impact and purpose 
  • A competitive salary and a huge growth trajectory 
  • Work with the best in the industry 
  • Flexible work environment 
  • Diversity and creativity 

Disclaimer: We do not wish to be contacted by recruitment agencies. Our hiring process is managed in-house and the best way for candidates to express interest is by applying with your resume through our company website. 

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