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Senior AI Engineer

💼 Full-time🗓 2026-06-24

Core

Designing and evolving the intelligence engine for a real-time medical review platform to mitigate clinical and compliance risks.

Role type

Senior AI Engineer (LLM Systems)

Builds

Production-grade AI systems, LLM-powered pipelines, and agent architectures for clinical risk mitigation.

Domain

Healthcare / Clinical AI / Large Language Models

Deliverable

production ML models

Required skills

LLM system design, prompt engineering, RAG, tool calling, evaluation frameworks, AWS, software engineering

Preferred skills

agentic systems, structured extraction, latency optimization, hallucination mitigation

Technologies

AWS, LLMs, RAG

Responsibilities

Design and improve LLM-powered pipelines and agent architectures; Build robust evaluation and benchmarking frameworks; Optimize for latency, cost, and output quality; Implement retrieval-augmented generation (RAG) and tool-use strategies; Ship production-grade AI systems; Collaborate with backend and product teams.

Seniority

Senior, hands-on IC

Rewrite
## About the role We're hiring a Senior AI Engineer to own and evolve the intelligence engine powering Brellium. This role goes well beyond toy demos — it requires deep fluency in prompt design, evaluation frameworks, agentic systems, structured extraction, tool use, retrieval pipelines, and performance tradeoffs. The work is core: transforming raw session data into clinically structured outputs, with a constant focus on improving accuracy, latency, reliability, and cost efficiency at scale. ## Responsibilities - Design and improve LLM-powered pipelines and agent architectures - Build robust evaluation and benchmarking frameworks to measure output quality and regression - Optimize for latency, cost, determinism, and output quality across high daily volume - Implement retrieval-augmented generation (RAG) and tool-use strategies where appropriate - Ship production-grade AI systems – not experiments - Collaborate closely with backend and product teams to integrate AI capabilities into the core platform ## Requirements - 5+ years of professional software engineering experience - Hands-on experience building and shipping LLM-powered applications in production - Deep familiarity with LLM concepts: prompt engineering, structured extraction, function/tool calling, RAG, evaluation frameworks - Experience with AWS or comparable cloud infrastructure ## Nice to have - Have built and deployed LLM systems in production at meaningful scale - Understand modern agent patterns: tool calling, RAG, memory, orchestration - Think deeply about evaluation, hallucination mitigation, and reliability - Care about performance and operational excellence, not just capability - Are excited to build a core engine that operates at massive daily volume in a high-stakes clinical domain ## About the company Brellium's mission is a big one – to improve the standard of care across the US healthcare system. We've built AI-powered technology that helps healthcare providers deliver safer, higher-quality care - starting with the first real-time medical review platform built to fix clinical and compliance risks before they impact patients. Each year, 1 in 20 people in the U.S. experiences a medical diagnostic or compliance-related mistake. Most providers lack the time, staffing, and tools to mitigate these issues - so they go unnoticed, impacting care quality and increasing clinical and financial risk. Brellium is building the AI-powered platform that helps providers deliver safer, more consistent care by mitigating risk early and aligning patient visits with clinical best practices. Our goal is to give every provider in the U.S. the tools to deliver clinically excellent, data-driven care - at scale. Brellium was founded in 2021. Since then, we've grown to serve over 250,000 providers across all 50 states who use Brellium to take better care of their patients and ensure data-driven, compliant care. We're a Series A company with over $30MM in funding from First Round Capital, Left Lane Capital, and Menlo Ventures.
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