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Retrieval Augmented Generation Jobs in Seattle, WA

Connexions Data Inc

Gen AI Developer

Seattle, WA · On-site

$57.25 - $78.75/hr

Proficiency in RAG (Retrieval-Augmented Generation) techniques. Strong understanding of natural language processing (NLP). Experience with data preprocessing and model fine-tuning. Familiarity with ...

CYNET SYSTEMS

Senior AI Engineer - Privacy

Bellevue, WA · On-site

$70 - $75/hr

This role involves applying large language models, retrieval-augmented generation, multi-agent orchestration, and foundation model capabilities to automate and enhance privacy operations. Requirement ...

Saransh Inc

Senior AI Engineer - Privacy

Bellevue, WA · On-site

$117K - $162K/yr

Embedded within the Data & Intelligence organization's Privacy practice, this engineer will apply large language models (LLMs), retrieval-augmented generation (RAG), multi-agent orchestration, and ...

Verathon, Inc.

Software Engineer

Bothell, WA

$115K - $231K/yr

Build Retrieval-Augmented Generation (RAG) pipelines, vector search capabilities, and secure data connectors to enable Verathon-owned data usage. * Collaborate with AI Business Partners and other ...

Verathon, Inc.

Software Engineer

Bothell, WA · On-site

$115K - $231K/yr

Build Retrieval-Augmented Generation (RAG) pipelines, vector search capabilities, and secure data connectors to enable Verathon-owned data usage. * Collaborate with AI Business Partners and other ...

Verathon, Inc.

Software Engineer

Bothell, WA · On-site

$115K - $231K/yr

Build Retrieval-Augmented Generation (RAG) pipelines, vector search capabilities, and secure data connectors to enable Verathon-owned data usage. * Collaborate with AI Business Partners and other ...

Amazon

Data Scientist, WW Ops FP&A

Bellevue, WA

... Retrieval-Augmented Generation (RAG) systems and LLM-based solutions to enhance financial knowledge retrieval and decision support Proactively identify and solve challenges related to GenAI solutions ...

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Retrieval Augmented Generation information

What are the typical daily responsibilities of a Retrieval Augmented Generation engineer?

A Retrieval Augmented Generation engineer typically spends their day designing and implementing systems that combine information retrieval with advanced generative models, such as large language models. This includes fine-tuning models, integrating external data sources, developing vector search pipelines, and evaluating output quality. Collaboration with data scientists, machine learning engineers, and product teams is common to ensure the solutions meet user requirements and scale effectively. Additionally, RAG engineers often troubleshoot issues, monitor model performance in production, and stay informed about the latest advancements in AI and information retrieval.

What is a Retrieval Augmented Generation job?

A Retrieval Augmented Generation (RAG) job typically involves developing and optimizing AI systems that enhance text generation by incorporating external knowledge retrieved from relevant sources. Professionals in this field work on integrating retrieval mechanisms with large language models to improve the relevance, accuracy, and factual grounding of generated content. Common responsibilities include designing retrieval systems, fine-tuning language models, optimizing performance, and ensuring the seamless integration of factual data into AI-generated text. This role is highly interdisciplinary, involving expertise in natural language processing (NLP), machine learning, and information retrieval.

What are the key skills and qualifications needed to thrive in the Retrieval Augmented Generation position, and why are they important?

To thrive in a Retrieval Augmented Generation (RAG) engineering role, you need a solid background in machine learning, natural language processing (NLP), and experience with scalable information retrieval systems, typically supported by a relevant degree in computer science or a related field. Familiarity with tools such as Python, PyTorch or TensorFlow, vector databases, and search platforms like Elasticsearch is essential, along with practical experience deploying and tuning RAG pipelines. Strong problem-solving skills, a collaborative mindset, and effective communication abilities set outstanding professionals apart in this field. These competencies are crucial for designing, implementing, and optimizing hybrid retrieval-generation AI systems that address complex, real-world information needs.

What are the most commonly searched types of Retrieval Augmented Generation jobs in Seattle, WA? The most popular types of Retrieval Augmented Generation jobs in Seattle, WA are:
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What cities near Seattle, WA are hiring for Retrieval Augmented Generation jobs? Cities near Seattle, WA with the most Retrieval Augmented Generation job openings:
Retrieval Augmented Generation jobs near you
Infographic showing various Retrieval Augmented Generation job openings in Seattle, WA as of July 2026, with employment types broken down into 60% Full Time, and 40% Contract. Highlights an 80% In-person, and 20% Remote job distribution.

Generative AI Engineer / LLM Engineer

Connexions Data Inc

Seattle, WA • On-site

Other

Posted 6 days ago

Job description

This is a Generative AI Engineer / LLM Engineer role with a strong focus on RAG (Retrieval-Augmented Generation), NLP, and Python.

They want someone who can:

  • Build Generative AI applications
  • Develop RAG-based chatbots and AI assistants
  • Fine-tune LLMs
  • Work with Python
  • Deploy AI solutions in an Agile environment

Likely experience expected:

  • 8 years in AI/ML
  • 2 4 years specifically in Generative AI or LLMs (depending on the market and client expectations)
Job Description
Must Have Technical/Functional Skills
Experience in executing projects in Agile Framework
Proven experience in machine learning and deep learning frameworks (e.g., TensorFlow, PyTorch).
Strong programming skills in Python and familiarity with libraries such as NumPy, Pandas, and Scikit-learn.
Experience with generative models (e.g., GANs, VAEs, Transformers) and natural language processing.
Proficiency in RAG (Retrieval-Augmented Generation) techniques.
Strong understanding of natural language processing (NLP). Experience with data preprocessing and model fine-tuning.
Familiarity with evaluation metrics for RAG systems.
Knowledge of transformer architectures and training techniques.
Awareness of ethical considerations and bias mitigation strategies.
Understanding of autonomous decision-making algorithms.
Proficiency in the programming language Python.
Strong analytical and problem-solving skills.
Roles & Responsibilities
Qualifications:
Bachelor s or master s degree in computer science, data science or equivalent
Develop and implement generative AI models using frameworks like TensorFlow and PyTorch.
Build and optimize RAG (Retrieval-Augmented Generation) pipelines.
Work on NLP tasks such as text classification, summarization, and conversational AI.
Perform data preprocessing, cleaning, and feature engineering using Python libraries (NumPy, Pandas).
Fine-tune and optimize transformer-based models and LLMs for specific use cases.
Evaluate model performance using RAG and NLP evaluation metrics.
Develop and integrate machine learning models into applications.
Apply autonomous decision-making logic in AI-driven workflows where needed.
Generic Managerial Skills, If any
Good to have Manufacturing domain understanding
Excellent communication
Team collaboration
Documentation and knowledge sharing

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