Machine learning engineer with Google Cloud Platform

Need Sr candidates 

Role *Machine learning engineer with Google Cloud Platform*

Remote.

*About the job*

*Description*
- *Primary platform:* Google Cloud Platform for inference, deployment automation, experimentation, and sampling
- *Production integration:* Java-based streaming pipelines for model integration layer
- *Infrastructure:* Hybrid setup with on-premise streaming and Google Cloud Platform serving stacks
- *Distributed systems:* Working knowledge needed for debugging and end-to-end testing. Deep expertise not required
- *Machine Learning frameworks:* TensorFlow, PyTorch, JAX or similar frameworks

*Job Responsibilities*
- Solid foundation in ML inference, deployment, and quality testing
- Proven ability to quickly get up to speed on new and unfamiliar tech stacks – this is the most critical trait
- End-to-end problem-solving approach – ability to own an issue from model handoff to user-facing behavior
- Core ML knowledge to benchmark models and work with researchers
- Experience deploying models in cloud environments, preferably Google Cloud Platform
- Exposure to Java or JVM-based systems. Model integration is done 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 support production decisions
- Deploy models to Google Cloud Platform and integrate them into production applications and Java-based streaming pipelines
- Take ownership of deployment automation end-to-end – from model handoff to live serving
- Monitor model behavior in production for real end-users
- Design and run benchmarking, performance testing, and quality testing for ML models
- Perform model sampling to support quality evaluation and researcher feedback loops
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