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Hire RAG Engineers — Full-Time

Browse 368 RAG engineers available Full-Time for remote work. All profiles manually reviewed. No agencies, no noise.

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368 Full-Time RAG profiles
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Dhawal Umesh

Dhawal Umesh

Mid-level
Chicago, United States (UTC-6) Full Time Rate on request
AI & Machine Learning Engineers Automation & AI Workflows
python tensorflow sql langchain rag +3
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Uriel

Uriel

Senior
Toronto, Canada (UTC-5) Full Time, Part Time Rate on request
AI & Machine Learning Engineers NLP & Prompt Engineering
python tensorflow pytorch langchain docker +4
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Khaleelur

Khaleelur

Entry-level
Kolkata, India (UTC+5:30) Part Time, Full-time Rate on request
ML (Machine Learning) Computer Vision
python machine learning deep learning sql scikit-learn +5
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Khandu

Khandu

Junior
Kolkata, India (UTC+5:30) Full-time, Contract-to-hire Rate on request
Automation & AI Workflows AI & Machine Learning Engineers
python tensorflow rag keras gcp +5
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Gulshat

Gulshat

Senior
Nicosia, Cyprus (UTC+2) Full-time Rate on request
NLP & Prompt Engineering AI & Machine Learning Engineers
python pytorch scikit-learn openai api hugging face +5
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Aditya

Aditya

Senior
Kolkata, India (UTC+5:30) Full-time, Part-time Rate on request
AI & Machine Learning Engineers Data Science & Analytics
python pytorch langchain scikit-learn rag +4
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Mohsin

Mohsin

Senior
New York, United States (UTC-5) Full-time, Freelance Rate on request
Automation & AI Workflows AI Product & Strategy
python langchain openai api sql rag +4
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Tal

Tal

Senior
Dublin, Ireland (UTC+0) Full-time Rate on request
AI & Machine Learning Engineers AI Product & Strategy
python rag aws sql ai agents +5
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Diego

Diego

Senior
Bogota, Colombia (UTC-5) Full-time Rate on request
NLP & Prompt Engineering Automation & AI Workflows
ai agents openai api python prompt engineering azure +4
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Bushra

Bushra

Senior
Kolkata, India (UTC+5:30) Full-time, Part-time Rate on request
AI & Machine Learning Engineers Data Science & Analytics
python docker tensorflow pytorch langchain +5
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About this role

RAG engineers, Full-Time availability

RAG (Retrieval-Augmented Generation) is the architecture behind most production AI assistants that answer questions from company knowledge bases, documents, and real-time data. Skilled RAG engineers design the entire pipeline: chunking, embedding, retrieval, re-ranking, and response generation.

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