Senior AI Engineer — Production LLM Systems

Ravenna Software, Inc.; Seattle, Northern (WA, KY); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive Salary

Meaningful equity

Choose your setup

Flexible time off

Job summary

Ravenna Software, Inc. is seeking experienced engineers to design and ship production systems that blend LLM capabilities with traditional software components.

You will own end-to-end AI features across the stack, emphasizing reliability, observability, and performance in real product workflows. Ideal candidates have five+ years building production systems, hands-on experience with LLMs, prompts, context management, and vector search, and a strong bias toward experiment-driven improvements in a

Qualifications

• Minimum five years building production systems
• Experience designing prompts, managing context, and tool orchestration for LLMs
• Focused on observability, performance, and reliability in AI systems
• Familiarity with retrieval augmented generation and vector databases
• Ability to measure quality and guide system design with experiments
• Strong software engineering practices and scalable architectures

Responsibilities

• Build production AI systems with LLMs integrated into real product workflows
• Design reliable LLM architectures with safeguards and observability
• Develop evaluation frameworks and datasets to measure model performance
• Build retrieval pipelines, vector search, and knowledge systems
• Ship high quality product features with engineering excellence
• Maintain clean, well-tested code and scalable platform architecture

Skills

Strong engineering fundamentals

LLM systems experience

Evaluation mindset

Retrieval and embedding systems

Experimentation and iteration

AI system intuition

Job description

Ravenna Software, Inc. is seeking experienced engineers to design and ship production systems that blend LLM capabilities with traditional software components.

You will own end-to-end AI features across the stack, emphasizing reliability, observability, and performance in real product workflows. Ideal candidates have five+ years building production systems, hands-on experience with LLMs, prompts, context management, and vector search, and a strong bias toward experiment-driven improvements in a