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Remote Rag Jobs in Compton, CA (NOW HIRING)

FabFitFun

AI Solutions Architect

Beverly Hills, CA · On-site +1

$90K - $125K/yr

This role is based out of our HQ in Beverly Hills, CA and is not eligible for remote. We are ... GPT-4/Claude APIs, LangChain or similar frameworks, prompt engineering, RAG pipelines * Comfort ...

KBR

Senior Gen AI Developer

El Segundo, CA · On-site +1

$182K - $228K/yr

KBR is seeing a Senior Gen AI Developer to join our team in El Segundo, CA (remote). The MILSATCOM ... Proven experience building and optimizing Retrieval-Augmented Generation (RAG) pipelines using ...

KBR, Inc.

Senior Gen AI Developer

El Segundo, CA · On-site

$182K - $228K/yr

KBR is seeing a Senior Gen AI Developer to join our team in El Segundo, CA (remote). The MILSATCOM ... Proven experience building and optimizing Retrieval-Augmented Generation (RAG) pipelines using ...

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How much do remote rag jobs pay per hour?

As of Jun 25, 2026, the average hourly pay for remote rag in Compton, CA is $21.84, according to ZipRecruiter salary data. Most workers in this role earn between $18.32 and $23.17 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Remote Rag, and why are they important?

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What is a Remote RAG (Retrieval-Augmented Generation) specialist?

A Remote RAG specialist is a professional who works with Retrieval-Augmented Generation (RAG) systems, typically in the field of artificial intelligence and machine learning. RAG combines traditional information retrieval techniques with generative models like large language models to provide more accurate and contextually relevant answers to user queries. Remote RAG specialists often build, fine-tune, and maintain these systems while working from a remote location. They may also work on integrating RAG models into applications, improving retrieval accuracy, and customizing outputs based on user needs.

What are some common challenges faced by professionals working in a remote RAG (Responsible AI Governance) role?

Professionals in remote RAG roles often encounter challenges related to cross-functional collaboration and maintaining clear communication, especially when working across different time zones. Ensuring alignment on ethical AI standards and compliance requirements can be complex, as it typically involves coordinating with data scientists, legal teams, and business stakeholders. Staying current with evolving regulatory frameworks and best practices in AI governance is also essential, demanding continuous learning and adaptability. Building trust and rapport within a remote team can require extra effort, but leveraging digital collaboration tools and regular check-ins can help mitigate these challenges.
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Remote Rag jobs near you
Senior AI Workflow & Systems Engineer

Senior AI Workflow & Systems Engineer

TubeScience

Los Angeles, CA • On-site

$110K - $160K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 20 days ago

Job description

Senior AI Workflow & Systems Engineer
Build and run the AI infrastructure that powers every team at TubeScience.
Role: Senior AI Workflow & Systems Engineer
Location: Remote (Los Angeles based preferred)
Compensation: Remote $70,000-$120,000 | Los Angeles $110,000-$160,000
Reports to: VP of IS
Team: Information Systems
About TubeScience
TubeScience is a data-driven creative studio producing performance advertising at massive scale - and we're growing fast. We're looking for a Senior AI Workflow & Systems Engineer to be the most technically sophisticated AI builder in the company. You'll sit in IT but serve everyone - owning the infrastructure, deployments, and systems that make our AI initiatives real, and unblocking every team that's building on top of them.
The Role
This is a systems and deployment role for someone genuinely excited about where AI is taking enterprise engineering. You won't just design workflows - you'll own the infrastructure they run on, keep them running reliably, and be the expert other teams call when things break or they hit a wall.
You are the architect, the deployer, the maintainer, and the unlocker - all in one. When there's no PM driving an AI initiative, you'll step in and own it end-to-end.
What You'll Own
AI Workflow Engineering
- Build and deploy LLM-powered applications and agent-based workflows that eliminate manual effort across the company
- Design multi-step agentic pipelines - tool use, RAG, structured outputs - built for production, not demos
- Integrate AI workflows with TubeScience's existing systems via REST APIs, webhooks, and custom integrations
- Develop automation pipelines
- Evaluate emerging AI tooling and own build-vs-buy decisions
Infrastructure & Deployment
- Own deployment and management of AI workflows and applications on Vercel and cloud platforms
- Build and maintain the infrastructure that supports TubeScience's AI initiatives - including cloud-based agents, serverless functions, and supporting services
- Design for resilience: logging, error handling, alerting, and monitoring across all deployed systems
- Manage secrets, environment configs, and deployment pipelines across environments
- Align with engineering on architecture, scalability, and infrastructure decisions
Cross-Functional Enablement
- Serve as the go-to technical resource for teams across TubeScience building AI-powered workflows and apps
- Deploy, maintain, and improve departmental AI tools - owning the full lifecycle from build to production
- Debug and unstick builders across the company when they hit technical walls
- Translate team-specific business needs into precise technical requirements and actionable solutions
- Serve as final escalation for complex AI and systems issues teams can't resolve on their own
Ownership & Improvement
- Proactively audit AI systems and workflows for reliability issues, inefficiencies, and improvement opportunities
- When there's no dedicated PM on an AI initiative, step in: define the problem, scope the solution, and drive it to completion
- Prototype emerging AI tools and frameworks and bring the best ones into TubeScience's stack
- Document every system thoroughly so the company can run it confidently
What We're Looking For
Background & Experience
- 4-6+ years in software engineering, DevOps, or systems engineering - with hands-on AI/ML experience
- Strong foundation as a software, systems, or DevOps engineer who has grown into AI - not the other way around
- Proven experience deploying and managing production applications on Vercel, AWS, GCP, or equivalent
- Hands-on with LLMs, generative AI, and orchestration tools (n8n, Make, Zapier, LangChain, or equivalent)
- Proven REST API integration experience with solid edge-case handling
- Experience building or maintaining cloud-based agents and serverless infrastructure
Technical Skills
- Strong Python and/or JavaScript/Node.js - clean, production-grade code
- Solid understanding of deployment pipelines, CI/CD, environment management, and secrets handling
- Experience with vector databases and embedding-based retrieval
- Comfortable with cloud infrastructure (AWS and/or GCP) and cloud-native application patterns
- Familiarity with monitoring, logging, and alerting for production systems
Soft Skills
- Highly autonomous - identifies problems and ships solutions without waiting to be asked
- Effective communicator across technical and non-technical audiences
- Strong product instincts: can step into ownership of an initiative when there's no PM in the room
- Calm under pressure; reliable when other teams are blocked and need answers fast
- Comfortable working across many different teams and problem domains simultaneously
+ Bonus Points
- Experience with AI agent frameworks
- Background in high-volume performance advertising, media, or creative production
- Experience with AI in a production context
- Multi-step agentic pipeline design or large-scale workflow orchestration
- Experience with data pipelines or BI tooling
• Benefits
Health, Vision & Dental coverage
Unlimited PTO
401(k) + Matching
♥ Life Insurance
Paid Sick Days
Paid Parental Leav

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