Full Stack AI Engineer

Reveleer

2d•$175,000 - $200,000•Remote

About The Position

Reveleer is hiring a Full Stack AI Engineer to join a small team within its AI division. The team is developing the next generation of agentic models for clinical prospective suspecting. The role offers the opportunity to contribute to product expansion and support user growth through end-to-end software development, infrastructure work, and collaboration with clinical experts and engineers. The engineer will build experiences spanning backend services, data pipelines, and frontend interfaces; develop and maintain scalable cloud-native infrastructure; improve observability; and help ensure systems are clinically accurate and surface actionable insights. The role also involves ownership of production troubleshooting, from investigating alerts and identifying root causes through preventing recurring issues. The position is offered as a remote opportunity.

Requirements

At least 5 years of experience building high-quality code and infrastructure.
At least 3 years of experience with large-scale distributed systems, such as microservices, event-driven architecture, or pipeline orchestration.
At least 2 years of experience with AWS or Google Cloud.
Hands-on full-stack experience with backend services (Python, Node, or similar) and frontend interfaces (React, TypeScript, CSS).
Familiarity with containerization, cloud-native deployment patterns, and common architectural patterns for scalable systems.
Strong debugging skills, including tracing issues across services, reading logs, forming hypotheses, and resolving problems under pressure independently of an LLM.
Curiosity about recent generative AI advances and a thoughtful perspective on where AI tools are useful and where they are not.
High ownership and accountability for the systems built.

Nice To Haves

Experience in healthcare, clinical data, or value-based care.
Experience with pipeline orchestration tools such as Airflow, Prefect, or Dagster.
Experience with Firestore, BigQuery, or other GCP data services.

Responsibilities

Apply first-principles thinking to break down complex problems and deliver durable solutions.
Understand business needs and build end-to-end product experiences across backend services, data pipelines, and frontend interfaces.
Build, scale, and maintain infrastructure, including containerized microservices, orchestration pipelines, and cloud-native architecture.
Develop and continually improve observability of systems and infrastructure at scale.
Partner with clinical experts and engineers to build scalable, clinically accurate systems that surface actionable insights.
Identify and improve slow, fragile, or over-engineered systems.
Troubleshoot, diagnose, and resolve production issues, owning problems from alert through root cause analysis and prevention.