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:
o Define modular architecture for data preprocessing, model execution, and post-processing.
o 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:
o Optimize GPU/CPU utilization, batching strategies, and memory management.
o Plan for multi-model and multi-tenant scenarios.
* MLOps & Lifecycle Management:
o Implement model versioning, artifact registries, and deployment workflows.
o Set up monitoring, logging, and alerting for solver performance.
* Security & Compliance:
o 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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