We are seeking an AI Engineer (Data) to join a healthcare technology organization undergoing a major modernization of its legacy platforms and data infrastructure. This role will focus on building scalable data pipelines, enabling machine learning systems, and helping integrate AI capabilities into modern healthcare applications.
You will work closely with data engineers, software engineers, and product teams to transform legacy systems into a modern AI-enabled data platform supporting analytics, automation, and intelligent healthcare workflows.
This is a hands-on engineering role focused on data pipelines, ML infrastructure, and scalable AI systems in a cloud-native environment.
Responsibilities
Design and build scalable data pipelines that support machine learning and AI-driven applications
Develop and maintain ML-ready data infrastructure across structured and unstructured healthcare datasets.
Implement data ingestion, transformation, and feature engineering pipelines.
Build services that expose ML models and AI capabilities to internal applications and APIs
Work with engineering teams to integrate AI functionality into modernized healthcare platforms.
Help migrate legacy data architectures to cloud-native data platforms
Implement best practices for data governance, data quality, and observability
Collaborate with product and engineering teams to deliver AI-powered healthcare solutions
Contribute to the development of ML pipelines, model deployment workflows, and AI platform tooling
Required Experience
5+ years of experience in data engineering, machine learning infrastructure, or AI platform engineering
Strong experience building data pipelines and distributed data systems
Proficiency in Python for data processing and ML workflows
Experience working with cloud platforms (AWS, GCP, or Azure)
Experience with data processing frameworks (Spark, Airflow, or similar)
Familiarity with ML lifecycle tools and model deployment workflows
Experience working with large-scale structured and unstructured datasets
Understanding of API-based architectures and microservices
Strong problem-solving skills and the ability to work in modern distributed systems
Preferred Experience
Experience working with healthcare data platforms or regulated environments
Exposure to LLMs, NLP pipelines, or AI-driven applications
Experience with feature stores, vector databases, or ML platforms
Familiarity with modern data lake/lakehouse architectures
Experience integrating AI services into production applications
Experience working in modernization or platform transformation initiatives
