Role: MLOps Engineer
Location: Decatur, GA (Onsite)
Duration:14 Months

Design and manage MLOps workflows on Azure, including reproducible model training using Azure ML and Databricks.
Implement experiment tracking and versioning using tools such as MLflow or Weights & Biases.
Develop and maintain model registries to streamline deployment and lifecycle management.
Define and monitor evaluation metrics (e.g., Precision-Recall curves, mAP, IoU/Dice, time-to-review savings) and build intuitive dashboards for stakeholders.
Collaborate closely with data scientists and domain experts to achieve modeling KPIs and improve performance outcomes.
Partner with product and UX teams to design effective review interfaces, including annotation workflows and triage processes.
Work with subject matter experts (SMEs) to refine labeling strategies and define acceptance criteria.
Enhance model robustness by addressing domain shifts (e.g., varying environments, seasons, camera conditions).
Optimize inference performance for high-resolution image processing.

Proven experience managing end-to-end machine learning workflows, including data exploration, augmentation, model training, evaluation, and deployment.
Hands-on experience with experiment tracking tools such as MLflow or Weights & Biases.
Practical knowledge of at least one computer vision domain: image classification, object detection (e.g., YOLO, MMDetection), or segmentation (e.g., UNet, DeepLab, SegFormer).
Strong understanding of computer vision evaluation metrics such as precision/recall, PR curves, mAP, IoU, and Dice coefficient.
Experience with Azure services, including Azure ML for model training and Azure Blob Storage or ADLS for data management.
Experience in data operations and labeling, including defining labeling guidelines and ensuring quality through validation techniques (e.g., spot checks, inter-annotator agreement, active learning).
Strong collaboration and teamwork skills in fast-paced environments.
Excellent communication skills with the ability to clearly present experiments, trade-offs, and results.
Proactive mindset with a strong interest in learning new methodologies and state-of-the-art machine learning techniques.

Interested candidates can reach out to me at

ML Ops Engineer

Key skills