Principal Machine Learning Engineer – Production Systems

Senior/Principal ML Systems Architect (TensorFlow + Python)

Overview

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.

Responsibilities

• Architect the ML Solver Platform :
  • 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 :
  • Optimize GPU/CPU utilization, batching strategies, and memory management.
  • Plan for multi-model and multi-tenant scenarios.
• MLOps & Lifecycle Management :
  • Implement model versioning, artifact registries, and deployment workflows.
  • Set up monitoring, logging, and alerting for solver performance.
• Security & Compliance :
  • Apply best practices for secrets management, dependency scanning, and secure artifact storage.

Required Skills & Experience

• 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.

Preferred Qualifications

• 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.

Tools & Technologies

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

Why Join Us?

• 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.

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