Job Description
Job Description Role: 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 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 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.