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About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or another quantitative or related technical discipline, or an equivalent combination of education and 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 and/or TypeScript/JavaScript
More about this role
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.
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...
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