Description - Primary platform: Google Cloud Platform (inference, deployment automation, experimentation, sampling)
- Production integration: Java-based streaming pipelines (model integration layer)
- Infrastructure: Hybrid - on-premise streaming + GCP serving stacks
- Distributed systems: Working knowledge required for debugging and end-to-end testing (not deep expertise)
- Machine Learning frameworks: TensorFlow, PyTorch, JAX or similar
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.
Disclaimer: GlobalLogic estimates the starting pay range for this role to be performed remotely to be $125,000 to $135,000 and reflects base salary only. This pay range is provided as a good-faith estimate, and the amount offered may be higher or lower. GlobalLogic takes many factors into consideration in making an offer, including candidate qualifications, work experience, operational needs, travel and onsite requirements, internal peer equity, prevailing wage, responsibilities, and other market and business considerations.
Job 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.
What we offer 
