Principal Machine Learning Engineer – Production Systems

We are seeking a highly experienced ML Systems Architect to design and implement a scalable, production-grade architecture for our machine learning solver. This role bridges research prototypes and commercial deployment, ensuring reliability, maintainability, and performance in a mixed technology stack.

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• Architect the ML Solver Platform:
  
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  • Define modular architecture for data preprocessing, model execution, and post-processing.
    
  • Establish clear API contracts between Python/TensorFlow and C# services.
    
  
  
  
• Convert research code into robust, testable, and observable services.
  
• Implement CI/CD pipelines, automated testing, and reproducibility standards.
  
• Design REST/gRPC endpoints for cross-language communication.
  
• Ensure compatibility with C#/.NET services.
  
• Performance & Scalability:
  
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  • Optimize GPU/CPU utilization, batching strategies, and memory management.
    
  • Plan for multi-model and multi-tenant scenarios.
    
  
  
  
• MLOps & Lifecycle Management:
  
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  • Implement model versioning, artifact registries, and deployment workflows.
    
  • Set up monitoring, logging, and alerting for solver performance.
    
  
  
  
• Security & Compliance:
  
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  • Apply best practices for secrets management, dependency scanning, and secure artifact storage.
    
  
  
  

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• ML Frameworks: Expert in TensorFlow (TF2/Keras), experience with ONNX Runtime for inference.
  
• Programming: Advanced Python for ML; strong understanding of packaging, type checking, and performance profiling.
  
• APIs: Proficiency in gRPC/Protobuf and REST for cross-language integration.
  
• Performance Optimization: GPU acceleration (CUDA/cuDNN), mixed precision, XLA, profiling.
  
• Observability: Metrics, tracing, structured logging, dashboards.
  
• Security: SBOM, image signing, role-based access, vulnerability scanning.
  

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• Experience with ONNX Runtime Training, PyTorch, or hybrid ML architectures.
  
• Familiarity with distributed training strategies and multi-GPU setups.
  
• Knowledge of feature stores and data validation frameworks.
  
• Exposure to regulated environments and compliance frameworks.
  

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• ML: TensorFlow, ONNX Runtime, tf2onnx.
  
• APIs: FastAPI, gRPC.
  

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• Work on cutting-edge ML solutions integrated into commercial engineering software.
  
• Define architecture that scales across global deployments.
  
• Collaborate with a team of experts in ML, software engineering, and UI development.