About the role:

• Design, develop, and deploy production-grade machine learning models to enhance revenue cycle management and healthcare workflows.
• Optimize and scale ML inference pipelines for efficiency, latency, and reliability.
• Work with engineering teams to integrate AI solutions into cloud-based and on-prem environments, ensuring seamless deployment and scalability.
• Automate and maintain MLOps pipelines, including data preprocessing, model training, evaluation, and deployment.
• Implement monitoring and observability for ML models in production to ensure performance, drift detection, and continuous improvement.
• Stay at the forefront of LLM and voice AI advancements, leveraging state-of-the-art techniques to improve our AI stack.

About You:

• 5+ years of experience in machine learning engineering, AI infrastructure, or software engineering with a focus on ML deployment.
• Strong proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch, and Hugging Face Transformers.
• Deep understanding of model deployment and serving technologies (e.g., TensorRT, Triton Inference Server, ONNX, FastAPI).
• Experience with MLOps tooling (e.g., Kubeflow, MLflow, SageMaker, Vertex AI) and containerization (Docker, Kubernetes).
• Familiarity with cloud platforms (AWS, GCP, Azure) and deploying AI/ML services in production environments.
• Strong software engineering skills, with experience in designing scalable and maintainable ML systems.

Preferred Qualifications:

• You have experience working with large language models (LLMs) and optimizing generative AI workflows.
• You’ve worked with EHR systems (Epic, Cerner, Meditech) and understand healthcare data interoperability (FHIR, HL7, CDA).
• You’ve built real-time AI applications, including voice AI, speech recognition, or NLP pipelines.
• You have experience in vector databases (e.g., Pinecone, Weaviate) and retrieval-augmented generation (RAG) architectures.

What’s in it for you?

• The opportunity to build and scale AI models in production that directly impact healthcare efficiency.
• A role where engineering meets AI, giving you full ownership of ML deployment and optimization.
• Competitive salary, equity options, and benefits, including health, dental, and vision coverage.

Who we are: