We are modernizing Electronic Health Record and Clinical Analytics systems using LLMs and AI agents, helping clinicians focus more on patients and less on administrative burden.
We’re looking for highly skilled AI engineers to design and build high-scale, cloud-based data processing pipelines that ingest, transform, and analyze massive volumes of healthcare data with low latency, powering business insights and analytics across EHR and RCM systems.
You will leverage LLMs, AI agents, and modern data platforms to solve problems like clinical decision support, revenue optimization, and workflow automation while using AI-assisted development tools to accelerate delivery.
Mandatory Qualifications
BS/MS in Computer Science or equivalent.
4+ years of relevant software engineering experience.
Strong software engineering skills in Python/Java.
Strong knowledge of SQL.
Deep expertise in data engineering: ETL, data transformation, data modelling (Spark, SQL).
Experience building high-scale distributed data systems.
Cloud experience (OCI/AWS/Azure).
Experience with creating major new functionality in a software system all the way from design, through development and testing to production deployment.
Experience with collaborating across multiple functional areas to develop components that are part of a larger system.
Experience with LLMs, prompt engineering, and agent frameworks.
Experience with blending hands-on coding with smart adoption of AI-driven solutions to rapidly prototype, test, iterate, and deliver reliable code.
Experience using ChatGPT, Claude or similar models on a routine basis to improve productivity.
Preferred Qualifications
Experience with agentic architectures or GenAI platforms.
Background in healthcare or digital health systems.
Understanding of EHR systems and RCM workflows.
Familiarity with healthcare coding standards (ICD/CPT).
Build and enhance data pipelines, ETL workflows, and transformations.
Contribute to LLM/agent-based features and analytics use cases.
Work with EHR/RCM datasets and support KPI/dashboard development.
Learn and apply best practices in cloud, data engineering, and LLMOps.