Technical Support Engineer

CodeRound; Hinoba-an; On-site; PHP 400,000 - 600,000; Full time

Job summary

CodeRound is seeking a Technical Support Engineer with a strong focus on MLOps to provide enterprise-level assistance to our customers. You will diagnose and troubleshoot issues related to machine learning models, frameworks, and deployment pipelines to keep ML systems running smoothly.

You will deliver remote support, communicate clearly via email, chat, and phone, and collaborate with data science teams to resolve issues and share MLOps best practices.

Qualifications

• Experience diagnosing ML model issues and deployment pipelines.
• Knowledge of ML frameworks and related tools.
• Ability to communicate technical information clearly to clients.

Responsibilities

• Own customer issues related to ML applications and drive resolution.
• Research, diagnose, and troubleshoot ML software and hardware issues.
• Provide support for ML frameworks (TensorFlow, PyTorch) and tools (MLflow, Kubeflow).
• Assist with network configuration affecting ML performance and data access.
• Use remote desktop to provide immediate client support.
• Communicate via email, chat, and phone with clear guidance.
• Collaborate with Customer Success Manager and data science teams.
• Escalate unresolved issues to internal teams when needed.
• Document knowledge as notes and manuals on MLOps.
• Follow up with clients after troubleshooting.

Skills

Troubleshooting

Remote support

Communication

Customer focus

Tools

TensorFlow

PyTorch

MLflow

Kubeflow

SSH/Remote Desktop

Linux basics

Job description

Client: Provider of platform for machine learning model training and deployment. It offers tools for fine-tuning, deploying, and observing machine learning models.

Requirements:

We are seeking a Technical Support Engineer with a strong focus on MLOps to provide enterprise-level assistance to our customers. In this role, you will diagnose and troubleshoot issues related to machine learning models, frameworks, and deployment pipelines. Your goal will be to ensure the seamless operation of ML systems and to assist our clients in leveraging these technologies effectively.

Responsibilities:

• Ownership of Issues: Take ownership of customer issues related to machine learning applications and see problems through to resolution.
• Research and Diagnosis: Research, diagnose, troubleshoot, and identify solutions for software and hardware issues specifically related to machine learning environments.
• Technical Support: Provide support for ML frameworks (e.g., TensorFlow, PyTorch) and tools (e.g., MLflow, Kubeflow) used in model training and deployment.
• Network Configuration: Assist clients with network configuration issues that may affect ML model performance and data access.
• Remote Support: Use remote desktop connections to provide immediate support and guidance to clients facing technical challenges in their ML workflows.
• Communication: Communicate effectively via email, chat, and phone to provide clear instructions and technical manuals to clients.
• Collaboration: Work closely with the Customer Success Manager and data science teams to ensure seamless support and integration of ML solutions.
• Escalation: Properly elevate unresolved issues to appropriate internal teams, such as data scientists or software developers, when necessary.
• Documentation: Document technical knowledge in the form of notes and manuals, particularly focusing on MLOps best practices and troubleshooting steps.
• Follow-Up: Ensure all issues are properly logged and follow up with clients to confirm their ML systems are fully functional post-troubleshooting.