As a Machine Learning Operations (MLOps) Engineer, you will be responsible for designing, deploying, and maintaining scalable platforms for machine learning models, utilizing open-source tools such as Kubernetes, Kubeflow, and MLflow. Your role will span the entire ML lifecycle, ensuring smooth deployment, efficiency, and security from development to production.

Key Responsibilities:

• Build and scale machine learning platforms and automate pipelines for training, deployment, and monitoring.
• Deploy models across cloud and on-premises environments, manage model versions, and handle model retraining.
• Optimize infrastructure, implement containerization (Docker, Kubernetes), and ensure high-performance systems.
• Set up monitoring solutions to track model performance and troubleshoot issues.
• Collaborate with data scientists, engineers, and other teams to ensure smooth integration and deployment.

Required Skills and Experience:

• Proficiency in Python and experience with cloud platforms (AWS, GCP, Azure).
• Expertise in containerization, orchestration, and machine learning frameworks (TensorFlow, PyTorch).
• Familiarity with DevOps principles, CI/CD, and automation processes.
• Strong problem-solving, communication, and teamwork skills.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, or related field.
• Proven experience in machine learning deployment or a similar area.
• Certifications in cloud platforms or DevOps tools are a plus.

Why Join Us?

• Competitive salary and benefits.
• Opportunities for professional development and career growth.
• Collaborative and innovative work environment.
• Exposure to cutting-edge machine learning technologies.