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Remote Rag Jobs in El Cajon, CA (NOW HIRING)

TEKsystems

AI Engineer

San Diego, CA · Remote

$50 - $58/hr

This is an opportunity to work on modern LLM architectures, intelligent agents, enterprise RAG, and scalable AI applications in a fully remote environment. Job Type & Location This is a Contract ...

Stoel

e-Discovery Project Manager

San Diego, CA · Remote

$84K - $184K/yr

... and RAG. ESSENTIAL FUNCTIONS/RESPONSIBILITIES * As a subject matter expert for e-discovery, the ... remote work options. We offer competitive and robust medical, dental, vision, life long-term ...

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Remote Rag information

See El Cajon, CA salary details

$18

$22

$24

How much do remote rag jobs pay per hour?

As of Aug 25, 2026, the average hourly pay for remote rag in El Cajon, CA is $22.45, according to ZipRecruiter salary data. Most workers in this role earn between $18.85 and $23.85 per hour, depending on experience, location, and employer.

What is a Remote RAG?

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 the key skills and qualifications needed to thrive as a Remote RAG?

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What are some common challenges faced by professionals working in a remote RAG 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 Generative AI Engineer (Google Gemini & Vertex AI) - Remote

San Diego, CA • On-site, Remote

DivIHN
See more jobs at DivIHNexternal

$110K - $152K/yr

Contractor

Posted 7 days ago

Job description

For further inquiries about this opportunity, please contact one of our Talent Specialists, Justeen at (224)-394-4903
Title: Senior Generative AI Engineer (Google Gemini & Vertex AI) - Remote
Duration: 6 Months with possible for extension
Location: Remote
Candidates must be available to work West Coast (PST) hours.
Only W2 candidates are eligible for this position. Third-party or C2C candidates will not be considered.
Day to Day Responsibilities
  • RAG & Prompt Engineering: Craft and refine effective prompts for RAG, grounding, and context tuning to achieve optimal AI performance in product development.
  • High-Performance API Engineering: Develop asynchronous microservices (FastAPI) using Server-Sent Events (SSE) or WebSockets to stream real-time LLM responses to front-end UIs without backend timeouts.
  • Vector Database Infrastructure: Design, develop, and implement robust Vector Databases using LLMs and modern retrieval technologies to capture information from diverse engineering sources (PDFs, design docs, regulatory guidelines).
  • Data Extraction & Structuring Pipelines: Build and optimize pipelines to extract and structure multi-modal data (tables, text, images) from unstructured documents for LLM training, grounding, and runtime query execution.
  • LLM Fine-Tuning & Training: Fine-tune and train generative AI models using client's engineering data and domain knowledge to create high-accuracy, domain-specific models.
  • GenAI Application & Tool Development: Design and implement scalable backend APIs (FastAPI/REST) and UI integration interfaces so internal engineers can query knowledge bases and analyze data.
  • Automated Requirements Generation: Develop backend functionalities to automatically generate technical requirements from design documents, user stories, and system specification files.
  • Documentation & Knowledge Transfer: Thoroughly document architecture, code, REST endpoints, and model training procedures to enable seamless knowledge transfer to client's internal teams.
  • Cross-Functional Collaboration: Partner closely with Subject Matter Experts (SMEs), System Engineers, and V&V Test teams to optimize AI-powered workflows.
  • AI Guardrails, MLOps & Cost Governance: Implement hallucination checks, PII masking, and guardrails (e.g., NeMo Guardrails) for medical device context. Track token usage, latency, and costs using LangSmith or Vertex AI monitoring.
  • Collaborate closely with engineers and cross-functional teams to design and develop AI solutions.
  • Develop and integrate Generative AI applications and APIs.
  • Work with large datasets, document processing, and data pipelines.
  • Participate in problem-solving, solution design, and technical discussions.
  • Communicate effectively with team members and stakeholders.

Must-Have Qualifications :
  • Relevant Experience & STEM Foundation: 4+ years of professional software/ML engineering experience, with a dedicated AI/ML focus in the last 1-2 years.
  • Google Gemini / Vertex AI (Non-negotiable): Hands-on experience with the Gemini model family and Vertex AI, including deployment, grounding, and integration into production AI services. Hands-on experience with containerization (Docker) and deploying services via Cloud Run or GKE (Kubernetes).
  • Languages & AI Libraries: Proficiency in Python and modern ML/AI frameworks (PyTorch, LangChain, LangSmith) for building autonomous LLM agents, tools, and RAG pipelines.
  • Agent Building & Tool Calling: Proven experience building AI/LLM agents and tool-calling systems in Python against unstructured, multi-source data.
  • Context Engineering & RAG: Expertise in RAG pipelines, prompt engineering, context tuning, grounding, and Vector Databases (e.g., Milvus, Postgres/Pgvector). Clear understanding of advanced RAG architecture including Hybrid Search (Vector + Keyword), Re-ranking models, and semantic caching.
  • Unstructured Data Handling (Non-negotiable): Demonstrated ability to ingest, clean, extract, and structure text, tables, and images from unstructured documents (PDFs, design docs, regulatory files) for LLM training and usage.

Role Information
  • Generative AI Engineer role.
  • Experience in API development.
  • Hands-on experience with Google Gemini and Vertex AI.
  • Knowledge of Agentic AI frameworks and architectures.
  • Strong Python development skills, including experience with LangChain and related AI frameworks.
  • Open to candidates from any industry domain.

Required Skills :
  1. Full Stack Engineering - Building applications for AI-powered services (Backend APIs + Front-End Integration).
  2. Generative AI & LLM Platforms - 2+ years building RAG pipelines & LLM apps using Python, LangChain, and LangSmith.
  3. Agent Building & Unstructured Data - Building AI agents/tool-calling systems in Python and handling unstructured, multi-source data extraction (PDFs, docs).
  4. Strong hands-on experience in Artificial Intelligence and Generative AI
  5. Python programming expertise.
  6. Experience with LangChain and Agentic AI frameworks.
  7. Understanding data handling to pull data from PDFs, design docs, regulatory files

Preferred Skills :
  1. Direct experience architecting and serving custom REST APIs.
  2. Experience in regulated / compliance-sensitive domains (e.g., healthcare / medical device guidelines like FDA, ISO 13485).
  3. Experience integrating multiple LLM APIs beyond a single provider (OpenAI, Bedrock, Claude, or similar).

Additional Preferred Skills
  • Direct experience designing and deploying high-throughput REST APIs (e.g., FastAPI/Flask).
  • Familiarity with medical device development regulations and compliance (e.g., FDA guidelines, ISO 13485).
  • Experience integrating multiple LLM APIs beyond a single vendor (e.g., OpenAI, AWS Bedrock, Anthropic Claude).
  • Front-end development/integration experience for UI design (e.g., Streamlit, Gradio, React/Next.js integration).

Education Requirements:
  • Minimum Bachelors in Software/Computer/IT/Systems/Biomedical Engineering + 3 years

Required Testing:
  • Technical evaluation of Python proficiency, RAG architecture concepts, and API/Agent design.

Software Skills Required:
  • Languages: Python (AsyncIO, OOP), SQL.
  • AI & Agent Frameworks: PyTorch, LangChain, LangSmith, Vertex AI SDK.
  • API & Web Frameworks: FastAPI, Flask, REST APIs, Server-Sent Events (SSE).
  • Databases & Search: Milvus, Pgvector, Qdrant, Redis (caching).
  • Front-End Integration: Streamlit, Gradio, basic React/Next.js.
  • Testing & Guardrails: pytest, JUnit, LangSmith evaluation, NeMo Guardrails.
  • DevOps & Cloud: Docker, GCP (Vertex AI, Cloud Run, GKE), Git.

Required Certifications:
  • Professional certifications specific to AI/ML (e.g., Certified AI Professional / CAIP, Google Cloud ML Engineer) considered a plus.

Interview
  • Number of Interviews: 1,
  • Web Conference (Zoom/ TEAMs)
  • Live problem-solving and technical exercise during the interview.

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