What are the responsibilities and job description for the MLOps Lead position at Wise Skulls Corp.?
Job Details
Job Overview
We are seeking a highly skilled MLOps Lead to drive the architecture, development, and optimization of end-to-end MLOps solutions. In this role, you will act as a subject matter expert, providing hands-on leadership to engineering teams while shaping the technical vision for mission-critical projects. The ideal candidate is an innovative problem-solver with deep experience in building scalable, automated, and efficient ML pipelines especially for computer vision and unstructured data workflows.
Key Responsibilities
Lead the design, architecture, and implementation of robust MLOps solutions.
Build scalable pipelines for computer vision (CV) workloads including object detection and DeepStream-based model deployment.
Manage and optimize unstructured data (image/video), including metadata and storage strategies.
Develop and maintain CI/CD pipelines, MLOps frameworks, monitoring systems, and observability dashboards.
Collaborate with cross-functional teams to improve automation, deployment efficiency, and model lifecycle management.
Ensure best practices in DataOps and MLOps across the engineering ecosystem.
Required Skills & Experience
Core Skills
Strong experience with computer vision, especially object detection.
Expertise in handling unstructured data video/image processing, metadata management, and related services.
Solid background in data modeling for unstructured datasets (BigQuery, PostgreSQL).
Hands-on experience with DataOps, MLOps, CI/CD, observability, and monitoring for CV projects.
Key Tools & Technologies
Google Cloud Platform Cloud (mandatory; retail environment knowledge is a plus).
Airflow, MLflow, Git, Nvidia DeepStream.
Other modern CV technologies and frameworks.
Experience Requirement
8 years in software development.
4 years of direct, hands-on experience designing and implementing MLOps solutions.
Preferred Qualifications
Experience working with AWS or Azure.
Background in Agile/Scrum methodologies.
Experience with retail industry ML/CV use cases.
