Staff AI Engineer, GenAI
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Job Description
AI Engineer architecting and operationalizing AI/ML solutions at Teladoc Health. Collaborating with teams to ensure production-grade reliability for AI-driven products.
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.
Requirements:
- Bachelor's degree in Computer Science, Engineering, Machine Learning, or a related field; equivalent work experience is acceptable.
- 8+ years of experience in AI/ML engineering roles, with proven success in architecting and scaling production LLM/GenAI and ML systems.
- Experience deploying LLM and GenAI solutions including RAG, vector database integration, and agentic/tool‑augmented LLM systems (LangChain, MCP, or similar frameworks).
- Experience with Snowflake or Databricks, using one or both as core platforms for data processing or AI/ML workloads.
- Proven track record in MLOps/LLMOps, including CI/CD pipeline automation, model serving, monitoring, and governance, using modern AI infrastructure tools such as Docker, Kubernetes, Azure ML, MLflow, and Terraform.
- Proficiency in Python and SQL, with experience processing high‑volume datasets using big‑data tools such as Spark or equivalent distributed systems.
- Ability to collaborate in cross-disciplinary teams (engineering, product, compliance, security) and deliver impact in regulated industries.
Benefits:
- Flexible Vacation Policy
- 80 hours of Paid Sick, Safe, and Caregiver Leave annually
- Performance bonus
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