Senior AI Solution Engineer

Role: Senior AI Solutions Engineer

Location: Seattle (WA)

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