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 modelsfrom experimentation and training to deployment monitoring and continuous improvement in operational environments.

What Youll 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.

Requirements

• 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.

Nice to Have

• 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.

This is a key role at the intersection of AI data and infrastructure with direct responsibility for transforming advanced machine learning models into reliable scalable and observable production capabilities.

Interested candidates are welcome to apply or contact us for more details.

@Insert Technologies