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Retrieval Augmented Generation Rag Jobs in Santa Clara, CA

Omega Solutions Inc

Sr AI Developer

Sunnyvale, CA · On-site

$50 - $55/hr

Build and maintain Retrieval-Augmented Generation (RAG) solutions using embeddings, vector databases, and knowledge bases. * Develop data ingestion, transformation, and indexing pipelines for ...

New

Uare.ai

Software Engineer (Early Career)

Los Altos, CA · On-site

... retrieval-augmented generation (RAG) systems. • Collaborate closely with product and engineering teams to turn early prototypes into production-grade systems. • Help shape the future of AI ...

Nirmata

AI Software Engineer Intern

San Jose, CA

Familiarity with Large Language Models (LLMs) and Generative AI (GenAI) technologies including Retrieval-Augmented Generation (RAG) and model tuning. * Familiarity with SLMs: model design and fine ...

Uare.ai

Software Engineer (Early Career)

Los Altos, CA · On-site

... retrieval-augmented generation (RAG) systems. • Collaborate closely with product and engineering teams to turn early prototypes into production-grade systems. • Help shape the future of AI ...

Waymo

Senior Software Engineer, DevAI

Mountain View, CA · On-site

$213K - $263K/yr

Advanced AI/ML: Strong expertise in Large Language Models (LLMs), including techniques like prompt engineering and Retrieval-Augmented Generation (RAG) * Coding Excellence: Proficiency in ...

CYNET SYSTEMS

ML Engineer

Santa Clara, CA · On-site

$55 - $60/hr

Architect and implement Retrieval-Augmented Generation (RAG) pipelines to enhance model performance using external knowledge sources, including document chunking, embedding generation, and retrieval ...

Uare.ai

Software Engineer (Early Career)

Los Altos, CA · On-site

... retrieval-augmented generation (RAG) systems. • Collaborate closely with product and engineering teams to turn early prototypes into production-grade systems. • Help shape the future of AI ...

Saransh Inc

Junior Gen AI Engineer

Santa Clara, CA · On-site

Architect and implement Retrieval-Augmented Generation (RAG) pipelines to enhance model performance using external knowledge sources, including document chunking, embedding generation, and retrieval ...

Qualitest Group

Gen AI Architect

Santa Clara, CA · On-site

$74.50 - $98/hr

Architect enterprise-scale Generative AI solutions leveraging LLMs, embeddings, and retrieval-augmented generation (RAG) pipelines. * Design and implement scalable AI microservices and APIs ...

Hudson IT

AWS AI Engineer / Apply

San Jose, CA · On-site

$65.25 - $87.25/hr

This role centers on hands-on development of retrieval-augmented generation (RAG) systems, fine-tuning LLMs, and AWS-native microservices that drive automation, insight, and governance in an ...

ClifyX

Senior Agentic AI Builder

San Jose, CA · On-site

$107K - $136K/yr

... RAG - Practical experience implementing retrieval-augmented generation pipelines for context-aware AI applications • MCP (Model Context Protocol) - Practical experience with MCP client/server ...

Nextpower

Forward Deployed Engineer (AI)

Fremont, CA · On-site

Build solutions utilizing Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and other emerging AI technologies. * Evaluate AI applications ...

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How much do retrieval augmented generation rag jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for retrieval augmented generation rag in Santa Clara, CA is $23.78, according to ZipRecruiter salary data. Most workers in this role earn between $20.34 and $24.86 per hour, depending on experience, location, and employer.

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Retrieval Augmented Generation Rag jobs near you
Infographic showing various Retrieval Augmented Generation Rag job openings in Santa Clara, CA as of August 2026, with employment types broken down into 64% Full Time, 33% Part Time, and 3% Contract. Highlights an 62% Physical, 3% Hybrid, and 35% Remote job distribution, with an average salary of $49,466 per year, or $23.8 per hour.

Sr AI Developer

Omega Solutions Inc

Sunnyvale, CA • On-site

$50 - $55/hr

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Day-to-Day Responsibilities:
Agentic AI Platform Development
  • Design, build, and maintain production-grade agentic AI workflows using AWS Bedrock and modern AI frameworks.
  • Develop AI systems that combine enterprise knowledge, engineering documentation, and internal data sources to support hardware engineering workflows.
  • Implement multi-step reasoning, tool usage, and autonomous task execution within agentic workflows.
  • Design and optimize prompts, agent architectures, and evaluation frameworks to maximize AI performance and accuracy.
RAG & Knowledge Systems
  • Build and maintain Retrieval-Augmented Generation (RAG) solutions using embeddings, vector databases, and knowledge bases.
  • Develop data ingestion, transformation, and indexing pipelines for engineering documentation, design artifacts, and technical datasets.
  • Design retrieval strategies across structured and unstructured data sources, including graph and search-based retrieval systems.
  • Continuously improve retrieval quality, ranking, and response accuracy.
Engineering Productivity & Stakeholder Engagement
  • Partner with hardware engineers, data owners, and cross-functional stakeholders to identify productivity opportunities and prioritize features.
  • Gather feedback from engineering users and incorporate findings into future platform enhancements.
  • Help define product direction, roadmap priorities, and long-term architecture for the AI platform.
  • Serve as a technical leader, helping break down complex initiatives into executable workstreams.
Platform Reliability, Security & Operations
  • Implement Human-in-the-Loop (HIL) workflows, validation mechanisms, and governance controls.
  • Design secure AI systems using enterprise security best practices, including IAM, encryption, audit logging, and data protection controls.
  • Build scalable backend services, APIs, and microservices supporting AI workflows.
  • Debug, test, monitor, and optimize deployed AI solutions for performance, reliability, and cost efficiency.
  • Support CI/CD pipelines and production deployment processes for AI applications.
 
Required Qualifications:
Core AI & Agentic Development Experience
  • Proven experience building and deploying production-grade agentic AI systems.
  • Strong hands-on experience with AI workflow development using AWS Bedrock or comparable cloud AI platforms.
  • Experience building Retrieval-Augmented Generation (RAG) systems using embeddings, vector databases, and enterprise knowledge sources.
  • Experience implementing agentic workflows, multi-step reasoning, tool usage, and autonomous task execution.
  • Familiarity with Model Context Protocol (MCP) and integrating external tools and data sources into AI workflows.
  • Experience designing evaluation frameworks and validation mechanisms for AI systems.
Software Engineering & Backend Development
  • Strong Python development skills.
  • Experience building backend services, APIs, and microservices.
  • Experience with Flask or similar Python web frameworks.
  • Strong understanding of software engineering best practices, testing, debugging, and code quality.
  • Experience working with CI/CD pipelines and modern software delivery processes.
Cloud & Data Engineering
  • Experience with cloud-native application development in AWS or similar cloud environments.
  • Experience building data ingestion, transformation, and indexing pipelines.
  • Knowledge of AWS services including: Bedrock, Lambda, Step Functions, EventBridge, S3, DynamoDB, Kendra, Neptune (or similar graph databases)
  • Experience designing graph-enhanced or hybrid RAG architectures.
Security & Governance
  • Experience implementing Human-in-the-Loop (HIL) workflows and AI governance controls.
  • Understanding of secure AI system design, including:
    • IAM and least-privilege access
    • Encryption and key management
    • Audit logging
    • Data protection and PII handling
  • Experience designing reliable distributed systems and asynchronous workflow orchestration.
Candidate Profile
  • Demonstrated hands-on experience using agentic AI tools in real-world development environments.
  • Strong understanding of how to maximize AI effectiveness through prompt engineering, workflow design, and agent orchestration.
  • Ability to work independently, define technical direction, and collaborate effectively with cross-functional stakeholders.
  • Seniority measured by depth of relevant AI and agentic development experience rather than total years of software engineering experience.
 
Nice-to-Haves:
  • Experience with LangChain, LangGraph, LlamaIndex, Hugging Face, or similar AI frameworks.
  • Experience building agentic reasoning loops with tool usage through MCP.
  • Experience implementing HIL clarification workflows and human approval checkpoints.
  • Infrastructure-as-Code experience using AWS CDK (TypeScript preferred).
  • Familiarity with model governance frameworks, content classification, and AI safety controls.
  • Experience with dependency scanning, vulnerability management, and security gates in CI/CD pipelines.
  • Experience working in monorepo or multi-package development environments.
  • Background supporting engineering productivity tools, developer platforms, or internal engineering enablement initiatives.
  • Experience supporting hardware engineering, CAD systems, or engineering knowledge management platforms.
  • Data science or machine learning background with experience evaluating model performance and accuracy.