SynthBee Inc. Cloud Engineer Pembroke Pines, FL · Full time Company website

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This role has the following 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.

Troubleshoot technology systems 

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; ensuring smooth integration into existing systems. Partner on the end-to-end design of ML solutions, from data ingestion to deployment

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, contributing to the team’s technical growth by sharing insights, providing mentorship to other team members, and fostering 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.

You might be a good fit if you have the following KSAs

Knowledge

• Experience with AWS and other cloud platforms (Microsoft Azure, Google Cloud) in a professional setting.
• Familiarity with designing data intensive cloud architectures 
• Hands on experience with cloud infrastructure management, containerization (Docker, Kubernetes), and automated deployment workflows. 
• Solid foundation of cloud-based networking, storage solutions, and security protocols. 

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.