MLOps Technical Lead

HCL Technologies Limited; Kuala Lumpur; On-site; MYR 120,000 - 180,000; Full time

Job summary

HCL Technologies Limited in Kuala Lumpur seeks an experienced ML Ops Engineer to build, deploy, and maintain robust ML pipelines and operational frameworks. You will automate model lifecycle management and contribute to project success by implementing best practices and supporting process compliance.

The role requires strong Python scripting, experience with Kubeflow/Keras/TFX, and hands-on use of CI/CD tools (Jenkins, GitHub Actions, CircleCI).

Qualifications

• Solid proficiency in ML Ops, including automation of ML pipelines and model lifecycle management.
• Solid understanding of DevOps tools for workflow automation (Jenkins, GitLab CI/CD, CircleCI, GitHub Actions).
• Strong Python skills for scripting, data processing, and ML pipeline development.
• Experience with IaC tools Terraform and AWS CloudFormation for cloud resource management.
• Knowledge of monitoring/logging tools such as Prometheus, Grafana, ELK Stack, and Fluentd.
• Experience with version control systems (Git, GitHub, GitLab, Bitbucket).
• Ability to participate in technical discussions and support process compliance within teams.

Responsibilities

• Build, deploy, and maintain robust ML pipelines and operational frameworks.
• Automate model training, validation, and deployment using Kubeflow Pipelines, TFX, and MLflow.
• Apply DevOps practices to streamline CI/CD for ML workflows and monitor pipeline health.
• Provision and manage scalable cloud resources using Terraform and AWS CloudFormation.
• Integrate monitoring/logging for production environments with Prometheus, Grafana, and ELK Stack.
• Collaborate in architecture discussions and ensure compliance with team processes.
• Prepare status reports and support project closure activities.

Tools

Jenkins

GitLab CI/CD

CircleCI

GitHub Actions

Terraform

AWS CloudFormation

Prometheus

Grafana

ELK Stack

Fluentd

Git

GitHub

GitLab

Bitbucket

Job description

Kuala Lumpur, Federal Territory of Kuala Lumpur

Job Summary

This role is accountable for building, deploying, and maintaining robust machine learning pipelines and operational frameworks. The individual applies solid expertise in ML Ops and DevOps tools to automate workflows, optimize model lifecycle management, and ensure reliable delivery of ML solutions. They contribute to project success by implementing best practices, supporting process compliance, and providing technical input within the team.

Key Responsibilities

1. Implement and maintain ML pipelines using Python, MLflow, Kubeflow Pipelines, and TFX to automate model training, validation, and deployment processes.
2. Apply DevOps practices with Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to streamline CI/CD for machine learning workflows and monitor pipeline health.
3. Utilize infrastructure-as-code tools such as Terraform and AWS CloudFormation to provision and manage scalable cloud resources for ML workloads.
4. Integrate monitoring solutions like Prometheus, Grafana, ELK Stack, and Fluentd to track model performance, system metrics, and log analytics in production environments.
5. Ensure process compliance by using Git, GitHub, GitLab, and Bitbucket for version control and code management within the team.
6. Participate in technical discussions and feasibility studies to evaluate technical alternatives and support architecture best practices for ML Ops solutions.
7. Prepare and submit status reports to highlight progress, minimize risks, and support project closure activities.

Skill Requirements

1. Solid proficiency in ML Ops, including automation of ML pipelines and model lifecycle management.
2. Solid understanding of DevOps tools such as Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions for workflow automation.
3. Solid experience with Python for scripting, data processing, and ML pipeline development.
4. Solid knowledge of infrastructure-as-code tools like Terraform and AWS CloudFormation for cloud resource management.
5. Solid skills in monitoring and logging tools including Prometheus, Grafana, ELK Stack, and Fluentd.
6. Solid familiarity with version control systems such as Git, GitHub, GitLab, and Bitbucket.
7. Solid ability to participate in technical discussions and support process compliance within the team.

Other Requirements

Optional but valuable:

• AWS Certified DevOps Engineer
• Google Professional Machine Learning Engineer

Benefits

• Personal time off
• Maternity and paternity benefits
• Access to skills/higher education programs/resources
• Discounts on products and services via Benefit Box
• Participation in CSR programs and live life with a purpose
• Opportunities to grow and advance your career

Note: The benefits listed above vary depending on the nature of your employment and the country where you work. Some benefits may be available in some countries but not in all.