Job Description
<h3>📋 Description</h3> Architect and manage our core MLOps infrastructure for model training, validation, and high-availability inference serving.
Develop and own our CI/CD/CT (Continuous Integration, Delivery, and Training) pipelines to automate the testing and deployment of ML models.
Implement comprehensive monitoring and alerting for model performance, data drift, and system health to guarantee production stability and uptime.
Implement and maintain security best practices throughout the ML lifecycle, including data privacy, access management, and infrastructure hardening, in close collaboration with security and engineering teams.
Partner closely with the AI and Engineering teams to streamline workflows, remove bottlenecks, and empower them to deliver value faster. <h3>🎯 Requirements</h3> BS in Computer Science, a related technical field, or equivalent practical experience.
3+ years of professional experience in an MLOps, DevOps, or Software Engineering role with a focus on infrastructure.
Hands-on experience with at least one major cloud provider (e.g., AWS, GCP, Azure).
Strong proficiency with containerization and orchestration technologies (e.g., Docker, Kubernetes).
Demonstrated experience designing and implementing automated CI/CD pipelines from scratch (e.g., using Jenkins, GitHub Actions). <h3>🏖️ Benefits</h3> Offers Equity
Offers Bonus