Duties: What you will do in this role:
Facilitate and participate in the preparation of ML data sets and scoring processes
Develop and maintain a knowledge of real-time and snapshotted data sources from across the company, as well as Analytics team members responsible for various sources
Work with the Data Scientists to develop and implement key machine learning models
Develop and support the ML portion of the Enterprise Data Platform strategy to maximize the distribution of ML predictions and recommendations, including API-based ML scoring and data warehouse-based access
Incorporate workflows into ML Data Engineering processes, to automate, monitor, and produce alerts
Implement performance tuning and query optimization
Act as a resource to the Machine Learning team with regards to Data Lifecycle Management
Keep pace with latest technology across various databases, data lakes, warehouses etc.
Collaborate with Analytics team members to follow all documented architecture, design & deployment processes to ensure compliance with policies
Design and develop scalable, reusable data models
Skills: To be successful in this role you have:
Experience in Machine Learning, Data Engineering, ML Infrastructure, or related fields, with strong foundations in probability, statistics, and machine learning.
Hands-on experience with Databricks, including Unity Catalog, and cloud ML platforms such as Azure ML Studio, AWS, or Google Cloud Platform.
Experience building and scaling end-to-end ML systems, ETL/ELT pipelines, and data workflows using Python, NumPy, Pandas, and SQL.
Experience building scalable APIs and microservices using FastAPI, with Snowflake or equivalent data warehouse technologies.
Experience deploying production workloads using Docker and Kubernetes, with a strong understanding of scalable, reliable software engineering practices.
Bachelor’s degree in Computer Science, Computer Engineering, Mathematics, Statistics, or equivalent field, with strong communication and cross-functional collaboration skills.
Education: Skills & Background:
Bachelor's degree in a technical field such as computer science, computer engineering or related field required
5+ years of experience as AI/ML Engineer
Deep understanding of data science, including 5+ years of experience in the space
Ability to work as part of a team, as well as work independently or with minimal direction
Excellent written, presentation, and verbal communication skills
Collaborate with data architects, modelers and IT team members on project goals
Strong knowledge of SQL, Python, R, Snowflake