Senior AI Engineer in healthcare

A venture-backed healthcare analytics company is looking for a Senior AI Engineer. You will be working on a clinical decision-support system that analyzes patient records, lab data, and imaging metadata to assist physicians in risk prediction and treatment optimization.

The environment includes Python, PyTorch, Hugging Face Transformers, and scikit-learn for model development, with data pipelines built in Airflow and Spark. Models are deployed via Docker and Kubernetes on AWS (S3, EKS, SageMaker). Must-have skills include strong experience building and fine-tuning LLMs or deep learning models, working with structured and unstructured data, prompt engineering, and deploying models into production environments. Solid understanding of model evaluation, bias mitigation, and performance monitoring is required. Pluses include experience with healthcare datasets (HL7, FHIR), HIPAA-compliant systems, retrieval-augmented generation (RAG), and model optimization for latency and cost efficiency.

You will design, train, evaluate, and deploy machine learning and generative AI models that directly impact clinical workflows. You will collaborate with data engineers and product teams to define model objectives, improve inference pipelines, and ensure production reliability. You will also monitor model drift, retrain systems as needed, and implement governance and safety controls appropriate for regulated environments.

The company builds AI systems that improve real-world healthcare outcomes, not just dashboards. Engineers here work on meaningful problems where model accuracy and reliability directly affect patient care. The team values scientific rigor, practical deployment skills, and responsible AI development.