AI Data Scientist Team Lead - AI Platform Engineering
New
Work from home (Pennsylvania)Full-TimeLead
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Experience
- 8+ years in data science, ML engineering, or AI solution architecture, with at least 3 years in a technical leadership or engineering management role; Minimum of 6 years-Relevant experience*
- Required Skills
- AWSDockerPythonSQLData AnalysisKafkaMLFlowSparkCI/CDTerraformDatabricksMLOpsLangChain
Requirements
- 8+ years in data science, ML engineering, or AI solution architecture.
- 3+ years in a technical leadership or engineering management role.
- Demonstrated experience designing production ML/AI systems end-to-end.
- Strong fluency in Python and SQL.
- Hands-on experience with Databricks (MLflow, Unity Catalog, Spark).
- Hands-on experience with cloud-native ML infrastructure (AWS preferred).
- Experience architecting agentic AI systems, LLM applications, or RAG pipelines in production settings.
- Proven ability to translate ambiguous business problems into technical specifications.
- Track record of mentoring engineers across multiple specialties.
- Experience managing concurrent technical projects.
- Familiarity with healthcare data standards (HL7/FHIR) strongly preferred.
- Familiarity with regulatory requirements (HIPAA) strongly preferred.
- Experience with Epic integration points (FHIR, SDE) a plus.
- Bachelor's Degree (Required).
- MS or PhD in Computer Science, Data Science, or related quantitative field preferred.
Responsibilities
- Architect end-to-end AI solutions and lead the AI Platform team.
- Design scalable AI architectures spanning batch and real-time workloads.
- Gather and refine requirements from clinical informaticists, data scientists, and business stakeholders.
- Architect agentic AI systems, RAG pipelines, and multi-model orchestration frameworks.
- Serve as technical authority on end-to-end AI pipeline design across Databricks, cloud-native platforms, and Epic integration points.
- Drive build-vs-buy and technology selection decisions for emerging AI capabilities.
- Ensure AI systems adhere to healthcare security standards (HIPAA) and responsible AI principles.
- Lead multiple concurrent AI projects, managing scope, timelines, and technical risk.
- Mentor and develop direct-report engineers; provide formal performance input for matrixed engineers.
- Establish platform engineering best practices and conduct architecture reviews.
- Align technical execution with strategic goals and contribute data-driven insights.
- Coordinate cross-functional collaboration and champion scalable AI practices.
- Run team rituals such as daily standups, weekly planning, and architecture office hours.
View Full Description & ApplyYou'll be redirected to the employer's site
