About the Role:
- 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
Basic Qualifications:
- 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 developmentGreat 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 and container orchestration platforms
- Sound knowledge of ML pipelines (Kubeflow, Dagster, etc.)
- Strong Python programming skillsExperience in model development and training
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