Divergent
AI Solutions Engineer
About this role
Divergent seeks an AI Solutions Engineer in Torrance, CA to design and deploy generative AI systems while serving as the company's AI enablement lead. This dual-focused role combines building production-ready LLM and multimodal solutions with championing AI adoption across the organization through training, best-practice guidance, and self-service resources.
What you'll do
- Develop and productionize generative AI solutions using RAG and Agentic AI frameworks in collaboration with cross-functional teams
- Act as primary internal AI champion, providing training, workshops, and guidance to raise AI expertise across the company
- Create and maintain self-service resources including playbooks, tutorials, and reusable component libraries
- Implement usage monitoring and bias mitigation strategies to ensure responsible and compliant AI deployment
- Host office hours, coaching sessions, and webinars to support AI adoption and best practices
- Gather user feedback and translate it into product improvements for AI tools and platforms
What they're looking for
- Python programming
- Large Language Models (LLMs) and multimodal models
- Prompt engineering and RAG techniques
- AI Agent development and deployment
- ML/LLM libraries (vLLM, LangChain, PyTorch, HuggingFace)
- Production-scale AI systems design and scaling
- Technical communication for non-technical audiences
- Training development and delivery
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Divergent
Divergent builds advanced manufacturing platforms combining 3D-printed metal connectors, robotics, and AI to produce lightweight vehicles for aerospace and defense applications. The company is hiring structures engineers, AI software engineers, robotics systems engineers, and simulation engineers to develop its innovative DAPS technology and autonomous manufacturing automation systems.
View all jobs at DivergentLikely interview questions
- Walk us through a production AI Agent you've deployed end-to-end—what were the key challenges in scaling it, and how did you handle monitoring and bias mitigation?
- Describe your experience with RAG and prompt engineering. How have you optimized retrieval quality and model responses in a real-world application?