Pizza Hut Việt Nam ★ 3.6

About Yum!
We connect customers with our brands through apps, websites, kiosks, point- of- sale systems, and other digital dining experiences. Behind those experiences is a growing ecosystem of data, artificial intelligence, and machine learning solutions that support restaurant operations and customer engagement around the world.
Our story might surprise you. We are the world’s largest restaurant company, encompassing KFC, Taco Bell, and Habit Burger & Grill, but there is much more happening behind the scenes than frying chicken, baking pizzas, and serving tacos.
Job Summary
The successful candidate will have hands- on experience supporting cloud- based production systems and will be comfortable operating across machine learning, infrastructure, software engineering, and DevOps domains.
This role is responsible for supporting, maintaining, and improving production machine learning platforms, pipelines, APIs, and deployment environments. The engineer will independently investigate complex operational issues, coordinate incident response, and partner with Machine Learning Engineers, Data Scientists, AI Engineers, platform teams, and cloud engineering teams to restore service and improve system reliability.
We are seeking an experienced AI Support Engineer with a Machine Learning Engineering focus to join our 24/7 AI operations team.
In addition to responding to incidents, this role will help strengthen the operational maturity of our AI ecosystem by developing automation, improving observability, refining support processes, and identifying recurring issues that should be addressed through engineering changes.
Key Responsibilities
Production Operations and Reliability

Assess the operational impact and urgency of production issues and take appropriate action to restore services.
Support production readiness reviews and validate that new AI and machine learning services meet operational support requirements before release.
Identify recurring failure patterns and partner with engineering teams to implement permanent solutions.
Independently diagnose and resolve moderately complex issues involving model inference, data dependencies, deployment failures, service availability, latency, capacity, and infrastructure performance.
Monitor and support production machine learning pipelines, inference services, APIs, feature- processing workflows, and deployment environments.
Participate in an on- call rotation supporting a 24/7 production environment.
Perform detailed root cause analyses for incidents, document findings, and recommend corrective and preventive actions.

Incident and Problem Management

Provide recommendations to improve reliability, supportability, and incident response effectiveness.
Ensure incidents are tracked through resolution and that follow- up actions are documented and completed.
Serve as a primary responder for AI- and MLE- related incidents identified through monitoring platforms, automated alerts, or user reports.
Lead or coordinate the technical investigation of incidents within the role’s area of responsibility.
Maintain clear communication during incidents, including impact, status, mitigation actions, and expected next steps.
Analyze operational metrics such as mean time to acknowledge, mean time to resolution, incident volume, availability, and recurring failure categories.
Engage appropriate engineering, data, infrastructure, and vendor teams when cross- functional support is required.

Deployment and Platform Support

Assist with model and platform releases, including deployment validation, rollback support, and post- release monitoring.
Help manage production configurations, operational dependencies, and service- level requirements across development, staging, and production environments.
Partner with Machine Learning Engineers to improve deployment patterns, environment consistency, scalability, and recoverability.
Support CI/CD workflows used to test, package, deploy, and promote machine learning models and AI services.
Troubleshoot issues involving containers, Kubernetes workloads, cloud resources, infrastructure- as- code deployments, configuration, networking, permissions, and service dependencies.

Automation and Continuous Improvement

Automate common operational activities such as service recovery, endpoint scaling, health validation, deployment checks, log collection, and resource- management tasks.
Design and implement scripts, utilities, and automated workflows that reduce manual support effort and improve response times.
Improve monitoring, logging, dashboards, alerts, and operational telemetry to provide earlier identification of production issues.
Develop and maintain runbooks, troubleshooting guides, support procedures, and operational playbooks.
Recommend improvements to architecture, tooling, processes, and support models based on operational experience.
Contribute to reliability, resilience, capacity- planning, and disaster- recovery initiatives for AI and machine learning services.

Cross- Functional Collaboration

Provide technical guidance to junior support engineers and assist with knowledge transfer, troubleshooting practices, and operational procedures.
Communicate complex technical issues clearly to both technical and nontechnical stakeholders.
Collaborate with Data Scientists and AI Engineers to ensure new solutions are supportable, observable, and production ready.
Work with cloud, DevOps, cybersecurity, data engineering, and enterprise support teams to resolve issues spanning multiple technology domains.
Partner with Machine Learning Engineers to support the deployment, maintenance, and optimization of production models and machine learning infrastructure.

Yêu cầu công việc

Qualifications

Experience with machine learning platforms or MLOps tools such as MLflow, Kubeflow, Vertex AI, SageMaker, or Azure Machine Learning.
Ability to investigate application logs, infrastructure metrics, distributed systems failures, data- quality issues, and service dependencies.
Two or more years of experience in machine learning engineering, DevOps, site reliability engineering, cloud engineering, software production support, or AI system operations.
Strong working knowledge of at least one major cloud platform, such as AWS, Google Cloud Platform, or Microsoft Azure.
Experience independently supporting business- critical applications or services in a production environment.
Ability to participate in an on- call rotation, including occasional support outside standard business hours.
Hands- on experience with container technologies and orchestration platforms such as Docker and Kubernetes.
Strong analytical, troubleshooting, and communication skills.
Experience with incident management, problem management, root cause analysis, and production change management practices.
Proficiency with Python and SQL for troubleshooting, scripting, data investigation, and automation.
Ability to manage multiple priorities and make sound technical decisions in a fast- paced, 24/7 support environment.
Experience supporting CI/CD pipelines and infrastructure- as- code tools such as Terraform, CloudFormation, or equivalent technologies.
Bachelor&039;s degree in computer science, Engineering, Information Technology, Data Science, or a related field, or equivalent practical experience.

Preferred Qualifications

Experience supporting globally distributed systems, teams, or customers.
Familiarity with secure software development practices, identity and access management, secrets management, and cloud security controls.
Experience mentoring junior engineers or leading technical incident investigations.
Experience with monitoring, observability, and alerting tools such as Prometheus, Grafana, Datadog, Splunk, Cloud Monitoring, or CloudWatch.
Understanding the complete machine learning lifecycle, including data preparation, model training, validation, deployment, monitoring, retraining, and retirement.
Familiarity with service- level indicators, service- level objectives, error budgets, and site reliability engineering practices.
Experience with model monitoring, data drift, model drift, performance degradation, and AI- specific operational risks.
Experience developing production- quality automation using Python, Bash, or another scripting language.
Experience supporting real- time inference services, batch- scoring pipelines, generative AI applications, or AI agents.

Attractive Benefits:

Five “Recharge Days” – Extra days, in addition to company holidays.
Full salary insurance
2 days WFH/ week
1 day off for birthday
Regular engagement activities: sport clubs, internal event…
100% salary during probation period
Advanced health insurance (Generali)
Annual Leave: 18 days/ year
Support Macbook and Monitor
Flexible Friday afternoon
13th- month bonus

Cập nhật gần nhất lúc: 2026-08-28 20:20:03