Cloud Engineer

About the role

The Cloud Engineer plays a pivotal role in supporting tech leadership by optimizing, deploying, and performing cloud-based solutions. The role involves assisting in cloud system upgrades, security patches, and infrastructure management tasks, as well as participating in cloud platform and machine learning model performance monitoring.

Responsibilities

• Cloud Systems - Assist in cloud system upgrades, security patches, and infrastructure management tasks under the guidance of senior engineers.
• Performance Testing - Participate in cloud platform and machine learning model performance monitoring to proactively identify and resolve potential issues.
• Troubleshooting - Identify and resolve technical issues related to machine learning workflows, cloud infrastructure, and system integrations.
• Production-Ready ML Systems - Support the deployment of robust ML models in production, ensuring high availability and scalability on cloud platforms like AWS, GCP, or Azure; build and refine ML pipelines that handle complex data workflows and large-scale datasets; ensure smooth integration into existing systems.
• Collaboration and Alignment - Partner with engineering teams to deploy, scale, and monitor ML operations and cloud-based solutions.
• Documentation and Knowledge Sharing - Develop and maintain detailed documentation for testing procedures and workflows, contribute to the team's technical growth by sharing insights, provide mentorship to other team members, and foster a collaborative environment.
• Continuous Learning and Innovation - Stay up to date with the latest cloud technologies, machine learning operations trends, and best practices to continuously improve support offerings, prototype, and evaluate emerging technologies to maintain a competitive edge.
• Risk and Compliance Management - Ensure cloud systems comply with security and regulatory standards, particularly in handling sensitive data and critical applications.

Requirements

• Knowledge - Knowledge of AWS and other cloud platforms (Microsoft Azure, Google Cloud) in a professional setting.
• Experience - Hands-on experience with cloud infrastructure management, containerization (Docker, Kubernetes), and automated deployment workflows.
• Skills - Hands-on experience with cloud-based machine learning services (AWS SageMaker, Azure ML, Google AI Platform); skilled with utilizing CI/CD pipelines for machine learning deployment; proficient in developing scalable software solutions and seamlessly integrating ML models into production; familiarity with common programming languages (Python, Java or JavaScript).
• Abilities - Ability to work within a large-scale, cross-functional project team independently with minimal supervision; excellent communication skills to convey complex technical concepts to both technical and non-technical stakeholders; ability to develop new features and infrastructure in support of rapidly emerging business and project requirements; ensure application performance, uptime, and scale, and maintain high standards for code quality and application design; strong analytical, problem-solving, and communication skills.

Qualifications

• Education - Bachelor's degree in Computer Science, Information Technology, or a related field.
• Experience - Minimum 5 years of experience in cloud engineering or related field.

Benefits

• Competitive salary
• Flexible work schedule
• Professional development opportunities
• Health insurance
• Retirement plan

Pay

• $80,000 - $120,000 annually

Schedule

• Full-time