We are seeking a skilled MLOps Engineer to join our IT Services team, where you will play a pivotal role in bridging the gap between data science and production environments. This position is dedicated to streamlining the deployment lifecycle of machine learning models, ensuring they are robust, scalable, and efficient. The ideal candidate will leverage their expertise in automation and performance optimization to drive the successful integration of AI solutions into our business operations, directly contributing to the organization's technological advancement and operational excellence.
Responsibilities
Design and implement automated pipelines for machine learning model deployment and continuous integration.
Manage cloud infrastructure and utilize specialized MLOps tools to maintain system reliability.
Oversee model versioning and experiment tracking to ensure reproducibility and auditability of all data science projects.
Containerize applications using Docker and orchestrate workloads with Kubernetes for scalable deployment.
Diagnose and resolve production issues related to machine learning systems to minimize downtime.
Develop and maintain Python-based scripts and applications to support ML workflows.
Establish and enforce CI/CD practices specifically tailored for machine learning models to accelerate delivery cycles.
Requirements
Requirements:
Experience: 3-6 years of professional experience in relevant technical roles.
Technical Proficiency: Strong command of Python programming and experience with Docker and Kubernetes.
Domain Knowledge: Proven expertise in MLOps tools, cloud platforms, and CI/CD pipelines for machine learning.
Core Skills: Demonstrated ability in automation, performance optimization, model versioning, experiment tracking, and production troubleshooting.
Deployment: Hands-on experience with the end-to-end deployment of ML models in production environments.
Responsibilities:- Design, develop, and implement MLOps pipelines for the continuous deployment and integration of machine learning models.- Collaborate with data scientists and engineers to understand model requirements and optimize deployment processes.- Automate the training, testing and deployment processes for machine ...
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