Senior AI Solutions Engineer

Role

Senior AI Solutions Engineer

Location

Seattle, WA (C2C and Remote considered)

Job Summary

As an AI Solutions Engineer, you will play a crucial role in developing and deploying advanced software solutions using generative AI technologies. You'll work at the intersection of AI and enterprise systems, integrating large language models and retrieval‑augmented generation techniques to build scalable and efficient applications.

Key Responsibilities

• Software Development & Deployment

  • Develop scalable software solutions leveraging generative AI technologies.
  • Design and deploy solutions integrating LLMs and RAG into enterprise systems.
  • Build modular AI components, ensuring seamless integration with existing systems.

• Collaboration & System Design

  • Work closely with architects and senior engineers to implement GenAI system designs.
  • Adhere to established reference architectures and standards in AI development.

• Component Development & CI/CD

  • Contribute to the creation of reusable components, templates, and patterns.
  • Implement and maintain CI/CD pipelines, focusing on reliability and scalability.
  • Ensure observability of GenAI applications for continuous monitoring.

• System Evaluation & Troubleshooting

  • Participate in testing, evaluation, and performance tuning of AI systems.
  • Optimize prompts and troubleshoot deployed applications to ensure stability.

• Documentation & Knowledge Sharing

  • Produce clear technical documentation for implemented solutions.
  • Engage in knowledge sharing to foster team learning and improvement.

• Continuous Learning & Improvement

  • Stay updated with advancements in generative AI and apply new learnings.
  • Contribute to other duties/projects as assigned by management.

Required Experience & Skills

• Proficiency in building and deploying GenAI applications with LLM APIs and frameworks.
• Hands‑on experience with retrieval‑augmented generation (RAG) and vector databases.
• Familiarity with agent‑based systems and tool integrations.
• Experience with cloud platforms like AWS, Azure, or GCP, and containerized environments such as Docker and Kubernetes.
• Understanding of CI/CD pipelines and modern deployment practices.
• Exposure to model evaluation, prompt engineering, and performance optimization.
• Proven track record in contributing to shared engineering standards or frameworks.
• Experience in release management and CI/CD deployment best practices.