ML Ops Engineer

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

Job Description

• 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.

Required Qualifications

• 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