Lead Software Engineer (RCM)

PracticeTek

13d

About The Position

As a Lead Engineer on our RCM and Shared Services team, you will lead the design and delivery of AI and LLM-powered capabilities that transform revenue cycle workflows across our products and brands. You will build practical, production-ready ML and LLM/RAG solutions that help practices automate coding, claims management, denials workflows, and patient financial interactions. You’ll work with a modern stack (AWS-native services, Python, containerized workloads, vector databases) and leverage LLMs, RAG, embeddings, and semantic search to deliver accurate, context-aware experiences. Your work will power shared AI services that can be reused across brands, accelerating innovation while maintaining security, reliability, and regulatory compliance. This is a hands-on technical leadership role: you will write code, design systems, and mentor others, while partnering closely with product, RCM subject-matter experts, and engineering teams across the company.

Requirements

Several years of professional experience (typically 5+ years) building and shipping ML/AI or data-intensive systems in production; experience in a lead, senior, or staff capacity is preferred but formal “architect” or PhD-level research experience is not required.
Demonstrated experience owning ML systems end-to-end: data ingestion and preparation, experimentation, training, deployment, and production monitoring.
Hands-on experience with AWS AI/ML services (e.g., SageMaker, Bedrock, Lambda, Step Functions) and MLOps practices; deep specialization is not required, but you should be comfortable learning and extending existing patterns.
Strong knowledge of LLMs and LLM-based systems, including RAG architectures, embeddings, vector stores, and semantic search; experience applying these to real business workflows.
Proficiency in Python and modern ML/DL frameworks (e.g., PyTorch, scikit-learn); familiarity with transformer-based models and common open-source LLM tooling.
Solid understanding of model deployment, containerization, and CI/CD concepts (Docker; EKS/ECS or similar orchestration is a plus).
Practical experience with data engineering for ML (feature pipelines, schema versioning, data quality gates, batch/stream processing) in collaboration with data engineering teams.
Experience working in or around regulated or privacy-sensitive environments (healthcare, fintech, or similar) and an appreciation of security, compliance, and governance constraints.
Strong problem-solving and system design skills: able to architect solutions that are scalable, maintainable, and robust under real-world production load.
Effective communication and collaboration skills, with the ability to work closely with non-technical stakeholders such as RCM operations, finance, and clinical leaders.

Responsibilities

Lead the design and implementation of shared ML/AI services for RCM, including use cases such as claim triage, denials prediction, automated document understanding, financial insights, and workflow automation.
Develop and maintain end-to-end ML pipelines (data preparation, feature engineering, training, evaluation, deployment, and monitoring) with reproducibility, scalability, and cost efficiency in mind.
Build and optimize LLM-based workflows, including RAG, embeddings, vectorization, and semantic search, to deliver accurate, context-aware answers using practice, payer, and RCM data.
Design and implement AWS-native AI pipelines leveraging services such as Lambda, Step Functions, SageMaker, Bedrock, and AgentCore, integrated into our broader platform architecture.
Collaborate with Product, RCM Operations, Data Engineering, and other engineering teams to translate business problems into ML/AI solutions and prioritize high-ROI RCM use cases.
Prototype and deploy ML/DL models for structured transformations, ranking, prediction, and workflow decisioning, with an emphasis on measurable business impact (e.g., days in A/R, denial rate, collection rate).
Implement observability and monitoring for models in production, including data quality checks, drift detection, guardrails for LLMs, and feedback loops from users.
Ensure all AI/ML solutions adhere to security, privacy, and compliance standards (including HIPAA and, where relevant, PCI), with appropriate handling of PHI and access controls.
Mentor and lead engineers by providing technical guidance, code reviews, and best practices for ML/AI development, MLOps, and LLM/RAG patterns.
Contribute to and maintain technical documentation, architectural diagrams, and playbooks for ML and LLM services to enable efficient onboarding and cross-team adoption.