Senior AI Applied Engineer

Become a part of our caring community   Most AI engineering jobs are a thin wrapper around a model API. This role is different. We build the platform that transforms millions of clinical documents into trusted, actionable data. Our systems use large language models (LLMs) to read medical records, extract structured facts, answer complex questions with citations back to the source document, and route ambiguous cases to human experts for review. Our users make decisions that impact real healthcare outcomes, so “good enough” is not good enough. Building AI systems that are accurate, reliable, auditable, and scalable is at the core of this role. As a Senior AI Applied Engineer, you will design, build, deploy, and operate production AI systems used at scale within one of the largest health insurers in the United States. You will own solutions end-to-end, from user experience and APIs to model orchestration, evaluation frameworks, infrastructure, and production operations. Why Join Us Build production AI systems where LLMs are in the critical path, not just demos or proofs of concept. Work on extraction, retrieval, agentic workflows, and human-review systems that process real healthcare data at scale. Own projects end-to-end across frontend, backend, AI orchestration, infrastructure, deployment, and operations. Solve challenging problems around accuracy, explainability, traceability, and reliability in regulated environments. Ship quickly in a small, high-impact team that embraces AI-assisted development and rigorous quality standards. Build systems that continuously improve through expert feedback, evaluations, and human-in-the-loop workflows.

Design, develop, and deploy full-stack AI-powered applications, including frontend experiences, APIs, backend services, and data pipelines. Build and optimize LLM-based workflows for information extraction, classification, retrieval, summarization, and agentic processes. Develop robust prompt strategies, structured output frameworks, tool-calling capabilities, and fallback mechanisms to improve reliability. Design and implement retrieval-augmented generation (RAG) systems using embeddings, vector databases, and semantic search technologies. Build evaluation frameworks, including benchmark datasets, regression testing, quality metrics, and human review workflows. Develop human-in-the-loop processes that enable clinicians and domain experts to validate AI outputs and provide feedback for continuous improvement. Build and maintain document-processing pipelines, including OCR, document ingestion, metadata extraction, indexing, and search capabilities. Optimize AI systems for accuracy, latency, reliability, scalability, and cost efficiency. Implement observability, monitoring, logging, alerting, and incident response processes for production systems. Deploy and operate services in cloud-native environments using Kubernetes, Docker, CI/CD pipelines, and modern infrastructure tooling. Collaborate with product, engineering, clinical, and operational stakeholders to translate complex business requirements into scalable solutions. Ensure solutions meet privacy, security, compliance, and auditability requirements within a regulated healthcare environment. Use your skills to make an impact   Required Qualifications Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience. 5+ years of experience building and operating production software systems. Experience developing backend services, APIs, distributed systems, or data-intensive applications. Hands-on experience integrating LLMs into production applications beyond simple chat interfaces, including structured outputs, tool calling, retrieval-augmented generation (RAG), agents, or multi-step workflows. Experience with at least one major AI platform or model provider such as OpenAI, Anthropic, Google, Azure AI, or AWS Bedrock. Strong programming skills in Python