Machine Learning Engineer (GCP)

GlobalLogic is a trusted digital engineering partner to the world’s largest companies, focusing on innovative digital products and experiences. They are seeking a Machine Learning Engineer to work primarily on Google Cloud Platform, responsible for deploying models, integrating them into production applications, and ensuring model performance in production environments.

Responsibilities:

• Strong foundation in ML inference, deployment, and quality testing
• Demonstrated ability to ramp up quickly on new and unfamiliar tech stacks — this is the single most important trait
• End-to-end problem-solving mindset — can own a problem from model handoff to user-facing behavior
• Core ML knowledge sufficient to benchmark models and collaborate with researchers
• Experience deploying models in cloud environments, ideally GCP
• Exposure to Java or JVM-based systems (model integration happens in Java; deep expertise not required)
• Familiarity with streaming data architectures
• Experience in hybrid cloud/on-prem environments

Requirements:

• Evaluate and benchmark new ML inference frameworks to guide production decisions
• Deploy models to GCP and integrate them into production applications and Java-based streaming pipelines
• Own deployment automation end-to-end — from model handoff through live serving
• Monitor how models behave in production for real end-users
• Design and execute benchmarking, performance testing, and quality testing on ML models
• Perform model sampling to support quality evaluation and researcher feedback loops
• Debug issues across the full stack — from inference layer down to streaming pipelines
• Partner with ML researchers to provide benchmarking feedback and guide inference decisions — requires enough core ML knowledge to have a meaningful technical handshake
• Adapt rapidly to non-standard and evolving tech stacks across hybrid (on-prem + GCP) infrastructure
• Bachelor's or Master's degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field
• Strong foundation in ML inference, deployment, and quality testing
• Demonstrated ability to ramp up quickly on new and unfamiliar tech stacks — this is the single most important trait
• End-to-end problem-solving mindset — can own a problem from model handoff to user-facing behavior
• Core ML knowledge sufficient to benchmark models and collaborate with researchers
• Experience deploying models in cloud environments, ideally GCP
• Exposure to Java or JVM-based systems (model integration happens in Java; deep expertise not required)
• Familiarity with streaming data architectures
• Experience in hybrid cloud/on-prem environments