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., * 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.

• Productionize ML Workflows:

• Convert research code into robust, testable, and observable services.
• Implement CI/CD pipelines, automated testing, and reproducibility standards.

• Integration & Interoperability:

• 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., * ML: TensorFlow, ONNX Runtime, tf2onnx.

• APIs: FastAPI, gRPC.
• DevOps: GitLab CI/GitHub Actions, Docker, Kubernetes.
• Monitoring: Prometheus, Grafana, OpenTelemetry.
• Security: HashiCorp Vault, Sigstore.

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.

• 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.
• Architecture: Proven experience designing scalable ML systems for production.
• APIs: Proficiency in gRPC/Protobuf and REST for cross-language integration.
• MLOps: CI/CD pipelines, containerization (Docker/Kubernetes), model registries, reproducibility.
• 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., * 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.