ML Engineer-Databricks

Jade Global•San Jose, CA

About The Position

Design and develop scalable machine-learning models and workloads on cloud ML platforms such as Azure ML Studio, AWS, or GCP. Facilitate and participate in preparing ML models and their scoring processes. Develop and support the DT GoToMarket strategy’s ML component to maximize distribution of ML predictions and recommendations, including publishing microservices and APIs. Incorporate workflows into ML Model Engineering processes to automate work, monitor performance, and produce alerts. Implement performance tuning and model optimization. Stay current on emerging ML platforms and technologies and recommend solutions to enhance existing systems or implementations. Collaborate with Product and Engineering team members and follow documented architecture, design, and deployment processes to ensure compliance with policies. The position requires a technical bachelor's degree or equivalent field and at least five years of AI/ML engineering experience. Candidates should have strong foundations in probability, statistics, and machine learning, and hands-on experience with Databricks, including Unity Catalog, and cloud ML platforms. The role involves building and scaling end-to-end ML systems, ETL/ELT pipelines, data workflows, APIs, and microservices, as well as deploying production workloads using Docker and Kubernetes. Compensation is determined by role, level, location, and job-related factors. The posting does not provide a specific salary range. Additional compensation may include benefits, discretionary bonuses, and equity.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Mathematics, Statistics, or an equivalent field; the posting also specifies a bachelor’s degree in a technical field such as computer science or computer engineering, or a related field.
5+ years of experience as an AI/ML Engineer.
Ability to work as part of a team and collaborate, as well as 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 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 to 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.