Tech Stack

Description

You will design and implement MLOps pipelines to automate model training, deployment, monitoring, and management. Lead and mentor a team of MLOps Engineers while collaborating with Data Scientists and ML Engineers to ensure models are production-ready, scalable, and maintainable. Drive the future of AI/ML infrastructure by developing cutting-edge platforms for responsible, reliable, and efficient machine learning operations.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field (or equivalent work experience)
• 4+ years of experience in the technology or data field
• Proficient engineering and coding skills in Python and other languages
• Strong background in AI/ML or Data Sciences technologies and platform development
• Great multi-disciplinary knowledge of technology space (data, cloud, ops, security)
• Excellent communication, leadership, and project management skills
• Proficient in Infrastructure as a Code (Terraform)
• Strong background in CI/CD workflows
• Mastery in containerization and container orchestration platforms
• Sound knowledge of ML pipelines (Kubeflow, Dagster, etc.)
• Strong Python programming skills
• Experience in model development and training

Responsibilities

• Design and implement MLOps pipelines to automate model training, deployment, monitoring, and management
• Lead/mentor a team of MLOps Engineers, fostering an inclusive and collaborative environment that encourages innovation and continuous learning
• Collaborate with Data Scientists and ML Engineers to ensure models are production-ready, scalable, and maintainable
• Develop strategies for model versioning, testing, and deployment to facilitate a seamless and efficient development lifecycle
• Monitor model performance and data drift, implementing automated alerts and processes for model retraining and optimization
• Develop and maintain platforms and applications that serve as foundational components for robust and scalable ML pipelines
• Stay at the forefront of MLOps trends, tools, and technologies, integrating new approaches to enhance our ML operations and infrastructure