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Ai Rag Jobs in Austin, TX (NOW HIRING)

Ascent360

Principal AI Architect (Austin)

Austin, TX · On-site

Model selection and fine-tuning Retrieval-Augmented Generation (RAG) Agent frameworks Data ... Define AI/ML technical capabilities, including: Generative AI systems Data pipelines Model ...

Visa

Senior Software Engineer

Austin, TX · On-site

$121K - $160K/yr

Experience in building Generative AI applications, conversational AI, RAG architectures, techniques and libraries * Experience or exposure to microservices and backend service development

Apple

Formal Verification - AI/ML Engineer

Austin, TX · On-site

$134K/yr

... RAG) pipelines, agentic tool-use frameworks, and domain-adapted models. Collaborating with formal ... Prototyping novel AI-driven approaches for tasks such as automatic SVA property synthesis, natural ...

Vestwell

Manager, Software Engineer (AI)

Austin, TX

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

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 ...

Apple

Senior AI Engineer

Austin, TX

$216K - $324K/yr

Description We're looking for a Senior AI Engineer with strong software development skills and a ... Hands-on experience with LLM APIs, embeddings, vector databases, and RAG workflows. Solid grounding ...

F-Prime Capital

Manager, Software Engineer (AI)

Austin, TX · On-site

$195 - $205/hr

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

Apple

Senior AI Engineer

Austin, TX

$216K - $324K/yr

Description We're looking for a Senior AI Engineer with strong software development skills and a ... Hands-on experience with LLM APIs, embeddings, vector databases, and RAG workflows. Solid grounding ...

Deloitte

AI Engineer Consultant

Austin, TX · Hybrid

We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ... Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ...

Tetra Computing

AI Architect

Austin, TX · On-site

$63 - $82/hr

Leverage Azure AI Search, vector databases, Semantic Kernel, and Retrieval-Augmented Generation (RAG) architectures. Establish AI governance, responsible AI frameworks, security controls, and ...

New

Deloitte

Google AI Lead Architect

Austin, TX

$54.75 - $75/hr

Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability. * Define end-to-end architectures across data ...

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)

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)

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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 Aug 8, 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 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 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 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 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 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 73% Full Time, 23% Part Time, and 4% Contract. Highlights an 65% Physical, 3% Hybrid, and 32% Remote job distribution, with an average salary of $57,733 per year, or $27.8 per hour.

Principal AI Architect (Austin)

Ascent360

Austin, TX • On-site

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Key Responsibilities
  • Architect, design, and evolve a scalable, secure AI platform and reference architecture that enables rapid development of AI-powered product capabilities
  • Drive strategic decision-making across: Model selection and fine-tuning Retrieval-Augmented Generation (RAG) Agent frameworks Data pipelines Inference optimization
  • Evaluate and integrate: Open-source and commercial LLMs Vector databases Feature stores MLOps platforms
  • Collaborate cross-functionally with Product, Engineering, Architecture, and UX to define AI requirements and priorities
  • Lead and influence engineering teams (directly and indirectly), fostering a culture of innovation and architectural excellence
  • Lead architecture reviews, technical governance, and long-term platform planning
Agentic AI & Advanced Systems
  • Provide architectural direction for Agentic AI systems, including: Workflow orchestration Multi-agent collaboration Context management Safety controls Autonomous decision-making frameworks
  • Design and implement LLMOps and AIOps practices for production systems
  • Drive observability practices for monitoring agent behavior and system performance
  • Define guardrails for: Agent interactions Memory usage Context boundaries
Governance & Standards
  • Define and enforce: Target-state architectures Principles and standards for AI/ML Responsible AI and ethical frameworks (e.g., GDPR, NIST AI RMF)
  • Establish processes for metadata extraction and management, enabling granular access control
  • Define AI/ML technical capabilities, including: Generative AI systems Data pipelines Model deployment strategies MLOps frameworks
  • Develop and maintain AI reference architectures and best practices
Qualifications Experience
  • 8+ years of experience as a Senior, Lead, or Principal Engineer/Architect
  • Hands-on experience with AI and ML systems in production environments
  • Proven ability to lead large-scale architectural initiatives and influence cross-functional decisions
Technical Skills
  • Strong programming proficiency in: Python (expert-level required) Java, TypeScript, Node.js, or similar
  • Experience with AI frameworks such as: LangGraph LangChain LlamaIndex Semantic Kernel AutoGen
AI / ML Expertise
  • Deep experience with: Large Language Models (LLMs) Embeddings Vector databases RAG architectures Model serving frameworks
  • Hands-on experience with Agentic AI patterns, including: Autonomous agents Tool usage Multi-agent coordination Goal-directed planning
Architecture & Platforms
  • Strong background in cloud platforms: AWS, Azure, or GCP
  • Experience with: Containerization Serverless technologies Distributed systems
Security & Systems Design
  • Experience designing secure AI systems, including: Data privacy Encryption Compliance Responsible AI practices
  • Solid understanding of: API integration patterns Messaging systems Event-driven architectures
Modern Architecture
  • Hands-on experience with: Microservices architectures Domain-driven design (DDD) Platform engineering
  • Proven experience building scalable, high-performance distributed systems
Leadership & Communication
  • Excellent communication, problem-solving, and technical leadership skills
  • Ability to influence and align teams across organizational boundaries
Why Join Us

This is an opportunity to work with cutting-edge AI technologies, solve real business challenges, and shape a forward-looking technology strategy.

If you are passionate about AI and ready to tackle complex and impactful challenges, we encourage you to apply.

Tricentis is proud to be an equal opportunity workplace. Qualified applicants will receive consideration for employment without regard to race, color, ethnicity, gender, religious affiliation, age, sexual orientation, socioeconomic status, or physical and mental disability and other statuses protected by law.

Global Sanctions Compliance

We comply with all applicable global sanctions and export control laws. Candidates must not be listed on any government restricted party lists (including OFAC SDN List and U.S. Commerce Department restricted lists) and must certify that their employment would not violate any sanctions or export control regulations. Candidates must notify us of any changes to their status during the application process or subsequent employment.

U.S. Work Authorization:

This role is not eligible for employer-sponsored work visas. Applicants must be authorized to work in the U.S. without current or future sponsorship.

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