Machine Learning/LLM Ops Engineer
4 days ago
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Manager - Talent Acquisition Specialist for Technology & EnablersThe ML/LLM Ops Engineer is specialized in AI industrialization and is tasked with managing the operational aspects of machine learning and deep learning models within the Bank's AI industrialization framework. Responsibilities include deploying models into production environments, monitoring their quality and performance, and ensuring scalability and reliability. Collaboration with data engineers, data scientists, and other stakeholders is crucial to streamline the model deployment process, optimize infrastructure, and implement best practices for model monitoring and management. Additionally, the ML/LLM Ops Engineer plays a key role in troubleshooting model issues, implementing automation solutions, and continuously improving the ML/LLM operations workflow.
Key Responsibilities:
- Setup, configure, and manage the production infrastructure (i.e. configure GPU, Batch size, security, and set other parameters or software required) to ensure optimal resource allocation for model training and inference.
- Implement best practices for version control, model reproducibility, and governance.
- Manage resources, model performance, model versioning, and infrastructure.
- Create and maintain documentation for model architecture, deployment processes, and operational procedures.
AI Implementation and Integration:
- Collaborate with data scientists and engineers to understand model requirements and optimize deployment processes.
- Design, develop, and implement MLOps pipelines for the continuous deployment and integration of machine learning models.
- Automate deployment pipelines: Develop and maintain CI/CD pipelines for data and machine learning models.
- Containerization: Use tools like Docker to containerize models for easier deployment across different environments.
- Ensure compliance with security and data privacy standards in all MLOps activities.
AI Monitoring and Maintenance:
- Automate the training, testing, and deployment processes for machine learning models.
- Set up monitoring for the ML pipeline and automated model retraining.
- Continuously monitor and maintain models in production, ensuring optimal computational performance, accuracy, and reliability by collecting data about the model performance on live data.
- Optimize machine learning pipelines for scalability, efficiency, and cost-effectiveness.
- Establish feedback mechanisms to gather insights from model performance and user interactions for continuous improvement.
- Troubleshoot and resolve issues related to model deployment and performance.
Others:
- Keep up-to-date with the latest MLOps tools, technologies, and trends.
- Provide support and guidance to other team members on MLOps practices.
- Ensure compliance as regulated in CIMB Niaga.
Qualifications:
- Bachelor's degree in Computer Science, Computer Engineering, or a relevant field.
- At least 5 to 7 years of experience as a Data Scientist, Data Engineer, and MLOps (machine learning or deep learning operations).
- Technical skills: CI/CD cycle (orchestrated by GitLab CI, GitHub Actions, Circle CI, or similar tools), DevOps pipeline (Docker and Kubernetes), including app performance monitoring, infrastructure settings, and configurations.
- Willing to be placed in Bintaro, Tangerang Selatan.
Mid-Senior level
Employment typeFull-time
Job functionInformation Technology, Analyst, and Science
IndustriesBanking, Financial Services, and IT Services and IT Consulting
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