Lead the design, development, and implementation of data frameworks, pipelines and services to support data operations for machine learning engineering teams
Collaborate with cross-functional teams to identify requirements and provide technical guidance
Integrate APIs with systems and platforms for seamless data exchange and enhanced system functionality
Produce well-engineered software, including appropriate automated test suites, technical documentation, and necessary operations
Boldly explore the bleeding edge of GenAI/agentic tools and frameworks to maximize gains while minimizing failures
Ensure consistent application of platform abstractions to ensure quality and consistency with respect to logging and lineage
Consult, educate, and coach/mentor on developer best practices and production standards, participate in code reviews and design sessions
Adhere to QMS framework and CI/CD best practices and helps to guide improvements to them that improve ways of working
Stay up-to-date on emerging technologies, trends, and best practices
Identify areas for improvement across the stack

Bachelor's or graduate degree in Computer Science, focused on software or data engineering, high-performance computing, or machine learning or 5+ years of industry experience in software/data/machine learning engineering
2+ years of industry experience in software engineering
Experience in Python
Experience in applying CI/CD implementations using git and a common CI/CD stack (e.g. Jenkins, CircleCI, GitLab, Azure DevOps)
Experience in API design principles, protocols, and tools (REST, GraphQL, Swagger, etc.)
Experience in data system/ETL design principles, protocols, and tools (REST, GraphQL, Swagger, etc.)
Experience developing infrastructure using Cloud services in GCP or similar cloud environment
Experience working with Docker and Kubernetes, or closely related containerization and cluster computing frameworks

Data Platform Engineer

Key skills