Senior ML Engineer: Cloud Deployment & Streaming

GlobalLogic; San Jose (CA); On-site; USD 130,000 - 140,000; Full time

Benefits offered by this job

Exciting Projects

Collaborative Environment

Work-Life Balance

Professional Development

Excellent Benefits

Job summary

GlobalLogic, a Hitachi Group Company, seeks an ML deployment engineer to own deployment automation from model handoff to live serving on Google Cloud Platform, integrating models into Java-based streaming pipelines. You will collaborate with ML researchers and benchmark inference frameworks.

The role emphasizes evaluating ML frameworks, ensuring robust end-to-end testing across distributed systems, and working in a remote-friendly, collaborative environment with strong benefits.

Qualifications

• Exposure to Java or JVM-based systems (model integration happens in Java, but deep expertise is not required).
• Familiarity with streaming data architectures.
• Experience operating in hybrid cloud and on-premises environments.

Responsibilities

• Demonstrated ability to learn and adapt rapidly to non-standard or unfamiliar technologies.
• Hands-on experience deploying models in cloud environments (Google Cloud Platform preferred).
• Working knowledge of distributed systems sufficient for effective end-to-end testing and debugging.
• Core ML knowledge to effectively benchmark models and collaborate with researchers.

Skills

Java / JVM exposure

Streaming data architectures

Hybrid cloud / on-prem environments

Core ML knowledge

GCP deployment experience

Distributed systems testing

Adaptability to new tech

Education

Bachelor’s or Master’s degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field

Job description

GlobalLogic, a Hitachi Group Company, seeks an ML deployment engineer to own deployment automation from model handoff to live serving on Google Cloud Platform, integrating models into Java-based streaming pipelines. You will collaborate with ML researchers and benchmark inference frameworks.

The role emphasizes evaluating ML frameworks, ensuring robust end-to-end testing across distributed systems, and working in a remote-friendly, collaborative environment with strong benefits.