Senior Applied AI Engineer

• Design and build LLM-powered product features used in production
• Develop agentic workflows and frameworks that coordinate multiple AI components
• Implement RAG (Retrieval-Augmented Generation) architectures using embeddings and vector search
• Build systems for prompting, context engineering, and tool usage
• Develop evaluation frameworks to measure LLM and agent performance
• Work closely with product and platform teams to turn AI capabilities into reliable, scalable product features
• Continuously improve system reliability, latency, and cost efficiency of AI pipelines

• Strong programming skills in Python and the ability to contribute to production-grade codebases
• Hands-on experience in LLMs, including at least some of the following: Training, finetuning, or post-training transformer-based models
• Building or operating LLM inference services in production, including performance work
• Experience with embeddings, vector databases, and semantic search
• Practical experience implementing RAG architectures
• Designing robust evaluations for agent workflows and generative systems, including metrics, error analysis, and human evaluation methods
• Experience building production-graded ML systems that can be deployed and operated, including pipelines, CI and CD practices, and monitoring
• Strong product mindset with the ability to translate ideas into working systems
• Clear communication and collaboration skills across research, engineering, and product
• A Master’s degree in computer science, engineering, mathematics, statistics, physics, or a related field, or equivalent professional experience

• Experience with healthcare data, clinical NLP, privacy and safety considerations
• Experience with Go

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