AI/ML Engineer - W2 Requirement

Role: AI/ML Engineer - W2 Requirement
Location: Santa Clara, CA (hybrid onsite)
Duration: Contract

Job Description:
Design and develop scalable ML models and workloads on cloud ML platforms such as Azure ML Studio, AWS, or Google Cloud Platform
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 publish microservices/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 would enhance current systems or implementation
Collaborate with Product and Engineering team members to follow all documented architecture, design & deployment processes to ensure compliance with policies
 
Skills & background:

Bachelor’s degree in Computer Science, Computer Engineering, Mathematics, Statistics, or equivalent field, with strong communication and cross-functional collaboration skills.
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
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 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.