Design, build and scale AI infrastructure and tooling that empowers data scientists, engineers and product teams across Shipt
Develop internal tooling and libraries using AI tools and frameworks
Create integrations with LLM gateways, agent frameworks, and structured output systems to improve AI-powered applications
Contribute to the design of scalable infrastructure for large-scale data processing and model serving
Partner closely with engineers and data scientists and collaborate with product and operations teams to deliver AI capabilities that power real-world logistics, retail and customer experiences
Requirements
Strong proficiency in Python
Hands‑on experience building with AI development tools such as MCP servers/clients, PydanticAI, LangGraph, LiteLLM, or similar AI platforms or tools
Background in developing infrastructure and tooling that supports engineering and data science teams
Experience with observability and monitoring systems for ML (e.g., MLflow, Weights & Biases, or similar)
Solid understanding of the data science workflow, from data ingestion and feature engineering to experimentation, deployment, and monitoring
Experience with cloud environments (AWS, GCP, or Azure) and containerized development (Docker, Kubernetes)
Have an AI first mindset and passion, with curiosity to learn and leverage AI in all aspects of your work