MLOps Engineer

MLOps Engineer – VISINT / EO / SAR

We are looking for an experienced MLOps Engineer to join an advanced AI unit operating in the field of Visual Intelligence (VISINT) and satellite imagery analysis, including Electro-Optical (EO) and Synthetic Aperture Radar (SAR) data.

In this role, you will bridge the gap between research and production by leading the complete lifecycle of machine learning models—from experimentation and training to deployment, monitoring, and continuous improvement in operational environments.

What You'll Do

• Design and lead end-to-end MLOps workflows covering data, training, evaluation, deployment, and monitoring.
• Build scalable pipelines for model training, inference, evaluation, and continuous retraining.
• Manage experiments, model versioning, artifacts, and model registries using tools such as MLflow, Weights & Biases, or DVC.
• Deploy machine learning models to production environments for both batch and real-time inference.
• Work with distributed computing infrastructure such as Kubernetes and Ray.
• Monitor model quality, performance, data quality, and model drift.
• Automate continuous evaluation and retraining processes.
• Optimize CPU and GPU utilization, model performance, infrastructure efficiency, and cloud costs.
• Collaborate with AI researchers, data engineers, software engineers, and DevOps teams to transition models smoothly from research to production.
• Improve the reliability, scalability, observability, and maintainability of production ML systems.
  
  

• Hands-on experience as an MLOps Engineer, ML Engineer, or Data Engineer with a strong machine learning orientation.
• Strong proficiency in Python.
• Experience working with machine learning and deep learning frameworks such as PyTorch or TensorFlow.
• Proven experience building model training and inference pipelines.
• Experience with experiment tracking and model management tools such as MLflow or Weights & Biases.
• Hands-on experience with Docker and Kubernetes.
• Experience working with cloud platforms such as AWS, Azure, or GCP.
• Strong understanding of the machine learning model lifecycle and the challenges involved in moving models from research to production.
• Ability to work effectively in a multidisciplinary and dynamic environment.
  
  

• Experience working with satellite imagery or SAR/EO data.
• Familiarity with orchestration and distributed processing tools such as Airflow, Prefect, or Ray.
• Experience working with GPU workloads and optimizing model training or inference performance.
• Familiarity with observability tools such as Prometheus, Grafana, and OpenTelemetry.
• Experience with data versioning tools such as DVC or with feature stores.
• Experience with real-time, mission-critical, or operational systems.