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Temporary Retrieval Augmented Generation Jobs in California

AI/ML technologies (e.g., LLMs, NLP, retrieval-augmented generation) - 6 Yrs of Exp * Jira, Azure DevOps, Confluence, and Smartsheet - 6 Yrs of Exp Must have Certifications: PMP, PMI-ACP, or ...

This role focuses on building robust Retrieval-Augmented Generation (RAG) pipelines to ensure AI agents and applications have access to the most relevant, timely, and high-quality information. You'll ...

This role focuses on building robust Retrieval-Augmented Generation (RAG) pipelines to ensure AI agents and applications have access to the most relevant, timely, and high-quality information. You'll ...

Retrieval-augmented generation (RAG) systems for dynamic hotel and travel searches * Preference engines that learn and evolve from member interactions * Lightweight AI orchestration layers integrated ...

The ideal candidate will have hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, vector databases, and cloud-native AI ...

Expertise in building Retrieval-Augmented Generation (RAG) pipelines * Knowledge of semantic layers and knowledge graphs for structured reasoning * Proficient in Python and FastAPI for backend and ...

Sr. Java Backend Engineer

Pleasanton, CA · On-site

$133K - $173K/yr

Experience with AI frameworks/tools such as LangChain , Spring AI , OpenAI APIs , Azure OpenAI , Vertex AI , Anthropic Claude , or Retrieval-Augmented Generation (RAG) . * Experience with Docker ...

Showing results 21-40

Temporary Retrieval Augmented Generation information

What is the difference between Temporary Retrieval Augmented Generation vs Data Scientist?

AspectTemporary Retrieval Augmented GenerationData Scientist
Required CredentialsTypically requires knowledge of AI, NLP, and some programming skillsRequires degrees in data science, statistics, or related fields, often with certifications in data analysis
Work EnvironmentOften project-based, working with AI models and large datasets in tech or research firmsUsually in corporate, research, or tech companies analyzing data to inform decisions
Industry UsageUsed in AI development, natural language processing, and machine learning projectsApplied across industries for data analysis, predictive modeling, and business insights

Temporary Retrieval Augmented Generation focuses on enhancing AI models with retrieval techniques, while Data Scientists analyze data to generate insights. Both roles require technical skills but serve different purposes within the tech and data ecosystem.

What are the most commonly searched types of Retrieval Augmented Generation jobs in California? The most popular types of Retrieval Augmented Generation jobs in California are:
What are popular job titles related to Temporary Retrieval Augmented Generation jobs in California? For Temporary Retrieval Augmented Generation jobs in California, the most frequently searched job titles are:
What job categories do people searching Temporary Retrieval Augmented Generation jobs in California look for? The top searched job categories for Temporary Retrieval Augmented Generation jobs in California are:
What cities in California are hiring for Temporary Retrieval Augmented Generation jobs? Cities in California with the most Temporary Retrieval Augmented Generation job openings:
Infographic showing various Temporary Retrieval Augmented Generation job openings in California 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.

Python Full Stack AI

Algo Soft Solutions LLC

San Jose, CA • On-site

Other

Posted 10 days ago


Job description

Key Responsibilities
Design, develop, and deploy AI/ML and Generative AI applications.
Build AI-powered assistants, copilots, chatbots, and autonomous AI agents.
Develop Retrieval-Augmented Generation (RAG) solutions using vector databases.
Integrate LLMs such as GPT, Claude, Gemini, or Llama into enterprise applications.
Fine-tune, evaluate, and optimize foundation models for business use cases.
Develop REST APIs and microservices for AI applications.
Implement prompt engineering, function calling, AI workflows, and agent orchestration.
Ensure AI solutions meet security, privacy, governance, and responsible AI standards.
Optimize AI model performance, latency, scalability, and cost.
Collaborate with product managers, architects, and data scientists throughout the software lifecycle.