AI/ML Engineer

Location: Santa Clara, CA

Duration: Contract

JD:

• 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 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 inComputer 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 inMachine Learning, Data Engineering, ML Infrastructure, or related fields, with strong foundations inprobability, statistics, and machine learning.
• Hands-on experience withDatabricks, includingUnity Catalog, and cloud ML platforms such asAzure ML Studio, AWS, or GCP.
• Experience building and scalingend-to-end ML systems, ETL/ELT pipelines, and data workflows usingPython, NumPy, Pandas, and SQL.
• Experience building scalableAPIs and microservices usingFastAPI, withSnowflake or equivalent data warehouse technologies.
• Experience deploying production workloads usingDocker and Kubernetes, with a strong understanding of scalable, reliable software engineering practices.