About the role
Join
the team leading the next evolution of virtual care. At Teladoc Health, you are empowered to bring your true self to work while helping millions of people live their healthiest lives. Here you will be part of a high-performance culture where colleagues embrace challenges, drive transformative solutions, and create opportunities for growth. Together, we’re transforming how better health happens. Job Description Summary of Position We are seeking an AI Engineer – Data & AI Platform to architect, build, and operationalize scalable generative AI and machine learning solutions across Snowflake, Databricks, and modern MLOps ecosystems. This role will partner with data scientists, ML engineers, and platform teams to design and deploy end-to-end AI/ML pipelines, advance AI integration, and ensure production-grade reliability for AI-driven products. The ideal candidate has 8+ years of experience in AI/ML engineering, with deep expertise in generative AI deployment, API-based AI services, large-scale data processing, and AI lifecycle management. The AI Engineer will not only deliver hands-on technical solutions but also set technical direction, mentor other engineers, and drive innovation across Teladoc's AI platforms. Essential Duties and
Responsibilities Operationalize GenAI and LLM applications, leveraging RAG (retrieval-augmented generation), vector search, prompt engineering, agentic AI, and MCP (Model Context Protocol). Lead the design, development, and deployment of production-grade LLM and ML pipelines, including data transformation, feature engineering, training, tuning, and serving. Architect scalable data and AI workflows on Snowflake, Databricks, and Azure ML, integrating AI models with modern data lakehouse platforms. Build and maintain API-based AI services (FastAPI, Flask), enabling secure, performant, and reliable model access at scale. Define and implement CI/CD pipelines for GenAI and ML services, using GitHub Actions/Azure DevOps, MLFlow, and container orchestration (Kubernetes, Docker). Develop and enforce MLOps/LLMOps best practices, including experiment tracking, model versioning, observability, and governance. Mentor ML engineers and data scientists on engineering rigor, scalable design, and production-readiness. Partner with cross-functional teams to integrate AI services into products, ensuring security, compliance, and resilience in regulated healthcare environments. Troubleshoot production AI systems, analyzing inference latency, drift, and performance issues, and implementing preventive solutions. Document and communicate architecture patterns, operational standards, and AI development frameworks across the organization. The time spent on each responsibility reflects an estimate and is subject to change dependent on business needs. Supervisory