ZipRecruiter

Log In

1

Genai Engineer Jobs in Seattle, WA (NOW HIRING)

Staffingine LLC

GenAI Engineer

Redmond, WA · On-site

GenAI Engineer Job Location: Redmond, WA Job Type: Contract * Design and implement RAG pipelines using hybrid search, vector, keyword, semantic ranking, and metadata enrichment * Apply chunking ...

Lululemon athletica

Staff AI/ML Engineer

Seattle, WA · On-site

  • Medical

  • Dental

  • Life

  • Retirement

  • PTO

Demonstrated ability to set GenAI engineering strategy including foundation model selection, fine-tuning infrastructure, and responsible AI standards * Track recordmentoring senior and staff ...

New

lululemon

Staff AI/ML Engineer

Seattle, WA · On-site

  • Medical

  • Dental

  • Life

  • Retirement

  • PTO

Core responsibilities As a Staff AI/ML Engineer, you will define the Technology AI/ML engineering ... and GenAI system architecture, operating as the highest individual contributor in the AI/ML ...

New

Lululemon

Staff AI/ML Engineer

Seattle, WA · On-site

  • Medical

  • Dental

  • Life

  • Retirement

  • PTO

Demonstrated ability to set GenAI engineering strategy including foundation model selection, fine ... tuning infrastructure, and responsible AI standards * Track recordmentoring senior and staff ...

New

next page

Showing results 1-20

All JobsGenai Engineer JobsGenai Engineer Jobs in Seattle, WA

Genai Engineer information

What are some typical challenges a GenAI engineer faces when deploying AI models in production environments?

GenAI Engineers often encounter challenges such as ensuring model scalability, addressing bias in generated outputs, and maintaining performance consistency in real-world applications. Deploying generative AI models requires careful monitoring to prevent unexpected or inappropriate outputs, as well as efficient resource management to handle large-scale computations. Collaborating closely with data engineers, product managers, and ML operations teams is essential to streamline deployment pipelines and quickly resolve issues that arise in live environments.

What is a GenAI engineer?

A GenAI Engineer is a professional who specializes in designing, developing, and deploying generative artificial intelligence (AI) models and applications. This role involves working with advanced machine learning techniques, such as large language models and generative adversarial networks, to create systems that can generate text, images, code, or other content. GenAI Engineers collaborate with data scientists, software engineers, and product teams to integrate AI capabilities into products and services, ensuring ethical use and scalability. They also stay updated on the latest developments in AI research to continually improve model performance and effectiveness.

What is the difference between Genai Engineer vs Data Scientist?

AspectGenai EngineerData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; experience with AI/ML frameworksDegree in Data Science, Statistics, or related fields; strong programming skills
Work EnvironmentDevelops AI models, fine-tunes generative AI systems, collaborates with AI teamsAnalyzes data, builds predictive models, interprets complex datasets
Employer & Industry UsageTech companies, AI startups, research labs focusing on generative AIFinance, healthcare, marketing, and tech firms analyzing data for insights

While both roles require strong technical skills and a background in data or AI, Genai Engineers focus on developing and deploying generative AI models, whereas Data Scientists analyze data to extract insights and build predictive models. The roles often overlap but serve different primary functions within AI and data-driven organizations.

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

To thrive as a GenAI Engineer, you need expertise in machine learning, deep learning, and programming languages such as Python, along with a solid understanding of generative models like GANs and transformers. Familiarity with frameworks such as TensorFlow or PyTorch, and experience with cloud platforms and MLOps tools, are highly valuable; advanced degrees or certifications in AI or data science are often preferred. Strong problem-solving, creativity, and communication skills help GenAI Engineers design innovative solutions and effectively collaborate with multidisciplinary teams. These skills ensure the development of robust, scalable generative AI systems that address complex real-world challenges.

What are popular job titles related to Genai Engineer jobs in Seattle, WA?

For Genai Engineer jobs in Seattle, WA, the most frequently searched job titles are:

What job categories do people searching Genai Engineer jobs in Seattle, WA look for?

The top searched job categories for Genai Engineer jobs in Seattle, WA are:

What cities near Seattle, WA are hiring for Genai Engineer jobs?

Cities near Seattle, WA with the most Genai Engineer job openings:

Genai Engineer jobs near you
Infographic showing various Genai Engineer job openings in Seattle, WA as of August 2026, with employment types broken down into 90% Full Time, 4% Part Time, and 6% Contract. Highlights an 88% Physical, 4% Hybrid, and 8% Remote job distribution.

GenAI Engineer

Staffingine LLC

Redmond, WA • On-site

Contractor

Re-posted 22 days ago

Job description

Job Title: GenAI Engineer
Job Location: Redmond, WA
Job Type: Contract

Job Description:

  • Design and implement RAG pipelines using hybrid search, vector, keyword, semantic ranking, and metadata enrichment
  • Apply chunking strategies (fixed-size, recursive, semantic, agentic) to optimize document segmentation for retrieval and embedding
  • Fine-tune and evaluate LLMs (e.g., GPT, LLaMA, Ollama) for tasks such as QA, summarization, NER, and sentiment analysis
  • Build multi-agent systems
  • Integrate AI capabilities into enterprise applications using Azure OpenAI, Cognitive Search, Azure Functions, and Power Automate
  • Collaborate with stakeholders to translate business needs into scalable AI solutions
  • Ensure model reliability, performance, and ethical compliance across deployments