Teladoc Health

AI Engineer

• 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.

Staff AI Engineer, GenAI