ML Engineer-Databricks

Jade Global•San Jose, CA

About The Position

The ML Engineer will design and develop scalable machine learning models and workloads on cloud ML platforms such as Azure ML Studio, AWS, or GCP. The role facilitates preparation of ML models and their scoring processes, and develops and supports the ML portion of the DT GoToMarket strategy to maximize distribution of ML predictions and recommendations, including publishing microservices and APIs. The position incorporates workflows into ML Model Engineering processes to automate operations, monitor performance, and produce alerts. It also involves performance tuning and model optimization, staying current on emerging ML platforms and technologies, and recommending solutions to enhance existing systems or implementations. The engineer collaborates with Product and Engineering teams and follows documented architecture, design, and deployment processes to ensure compliance with policies.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Mathematics, Statistics, or an equivalent field; a technical bachelor’s degree such as computer science or computer engineering is required.
At least 5 years of experience as an AI/ML Engineer.
Ability to collaborate as part of a team and to work independently or with minimal direction.
Excellent written, presentation, and verbal communication skills.
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 GCP.
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.

Responsibilities

Design and develop scalable ML models and workloads on cloud ML platforms such as Azure ML Studio, AWS, or GCP.
Facilitate and participate in the preparation of ML models and their scoring processes.
Develop and support the ML portion of the DT GoToMarket strategy to maximize the distribution of ML predictions and recommendations, including publishing microservices and APIs.
Incorporate workflows into ML Model Engineering processes to automate, monitor, and produce alerts.
Implement performance tuning and model optimization.
Stay current on emerging ML platforms and technologies and recommend solutions that enhance current systems or implementations.
Collaborate with Product and Engineering team members and follow documented architecture, design, and deployment processes to ensure compliance with policies.