Build and maintain scalable data pipelines and transformation workflows
Implement data quality checks, validation frameworks, and monitoring systems
Design and operationalize evaluation frameworks for datasets and ML outputs
Package and deliver production-ready datasets with clear documentation and QA standards
Develop ML-assisted tools and workflows to improve data processing and delivery efficiency
Generate, augment, and validate synthetic datasets to support client and internal use cases
Deploy lightweight ML/LLM-powered solutions to solve operational bottlenecks
Work directly with delivery teams to implement and maintain production workflows
Debug, troubleshoot, and resolve technical issues across data pipelines and systems
Continuously improve tooling, processes, and measurement approaches used in delivery
Identify and implement practical improvements that increase speed, reliability, and quality of delivery outcomes

5+ years in data science, data engineering, or ML engineering roles
Strong proficiency in Python and SQL
Hands-on experience with data tooling (pandas, Plotly, Streamlit, Dash)
Practical experience working with LLMs and deploying ML solutions in production environments
Experience integrating and working with APIs and technical systems
Strong problem-solving skills with a bias toward implementation and delivery
Excellent collaboration and communication skills in cross-functional teams

Senior Applied AI Engineer

Key skills