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Svitla Systems, Inc. Linkedin · Posted 3mo ago

MLOps Engineer

Romania

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Indexed description

Svitla Systems Inc. is looking for an MLOps Engineer for a full-time position (40 hours per week) in Romania. Our client develops intelligent devices and systems for real-time supply chain, robotics, and computer vision operations.

Requirements

Programming & Development:

  • Advanced proficiency in Python development
  • Working experience with PyTorch and/or TensorFlow
  • Ability to create maintainable, well-tested code with proper error handling
  • Experience creating RESTful services and model serving endpoints

MLOps Infrastructure

  • Design and implementation of automated ML workflows (training, evaluation, deployment)
  • Experience with model versioning, storage, and metadata management
  • Implementing dataset versioning and change management
  • Knowledge of model optimization and format conversion (ONNX, TFLite, TensorRT, SNPE)

DevOps & Infrastructure

  • Docker image building and optimization
  • Experience with Kubernetes and/or RunAI
  • GitHub Actions workflow design and implementation
  • GCP experience, particularly with Vertex AI, Cloud Batch, or similar services
  • GPU allocation and optimization for training workloads

Testing & Quality Assurance

  • Unit, integration, and E2E test implementation for ML systems
  • Model metrics calculation and performance benchmarking
  • Experience with testing ML models on edge devices

Additional Technologies

  • Experience with Hydra or similar configuration frameworks
  • Familiarity with W&B, MLflow, or similar tracking tools
  • Creating and maintaining Python packages and dependencies
  • Understanding of CV models, image processing, and related ML tasks

Responsibilities

  • Design and implement an end-to-end MLOps architecture for computer vision models
  • Create automated pipelines for model training, quantization, conversion, and evaluation
  • Develop a model registry with comprehensive versioning and metadata capabilities
  • Build systems for automated model testing across multiple target architectures
  • Implement data versioning and dataset management solutions
  • Maintain CI/CD pipelines for ML model lifecycle management
  • Optimize Docker environments for development and deployment
  • Develop Python libraries and APIs for internal model consumption
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