Values

The ad says“Hands-on experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization/orchestration technologies such as Docker and Kubernetes.”
The ad says“Hands-on experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization/orchestration technologies such as Docker and Kubernetes.”
The ad says“Experience implementing CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Jenkins, Azure DevOps) for data or ML projects.”
The ad says“Preferred experience with observability stacks (e.g., Prometheus, Grafana, ELK/EFK, Datadog) and setting up model and data quality monitors.”
The ad says“Preferred experience with observability stacks (e.g., Prometheus, Grafana, ELK/EFK, Datadog) and setting up model and data quality monitors.”
The ad says“Hands-on experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization/orchestration technologies such as Docker and Kubernetes.”
The ad says“Familiarity with LLM / GenAI workloads and associated tooling is a plus (even though the core MLOps role is not GenAI/LLM-specific).”
The ad says“Experience implementing CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Jenkins, Azure DevOps) for data or ML projects.”
The ad says“Experience implementing CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Jenkins, Azure DevOps) for data or ML projects.”
The ad says“Preferred experience with observability stacks (e.g., Prometheus, Grafana, ELK/EFK, Datadog) and setting up model and data quality monitors.”
The ad says“Experience implementing CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Jenkins, Azure DevOps) for data or ML projects.”
The ad says“Preferred exposure to streaming and near-real-time data processing (e.g., Kafka, Kinesis, Pub/Sub).”
The ad says“Preferred exposure to streaming and near-real-time data processing (e.g., Kafka, Kinesis, Pub/Sub).”
The ad says“Familiarity with ML frameworks and tooling (e.g., scikit-learn, TensorFlow, PyTorch, MLflow, Kubeflow, SageMaker, Vertex AI, or equivalents).”
The ad says“Familiarity with LLM / GenAI workloads and associated tooling is a plus (even though the core MLOps role is not GenAI/LLM-specific).”
The ad says“Familiarity with ML frameworks and tooling (e.g., scikit-learn, TensorFlow, PyTorch, MLflow, Kubeflow, SageMaker, Vertex AI, or equivalents).”
The ad says“Preferred experience with observability stacks (e.g., Prometheus, Grafana, ELK/EFK, Datadog) and setting up model and data quality monitors.”
The ad says“Preferred exposure to streaming and near-real-time data processing (e.g., Kafka, Kinesis, Pub/Sub).”
The ad says“Familiarity with ML frameworks and tooling (e.g., scikit-learn, TensorFlow, PyTorch, MLflow, Kubeflow, SageMaker, Vertex AI, or equivalents).”
The ad says“Familiarity with ML frameworks and tooling (e.g., scikit-learn, TensorFlow, PyTorch, MLflow, Kubeflow, SageMaker, Vertex AI, or equivalents).”
The ad says“Familiarity with ML frameworks and tooling (e.g., scikit-learn, TensorFlow, PyTorch, MLflow, Kubeflow, SageMaker, Vertex AI, or equivalents).”
The ad says“Familiarity with ML frameworks and tooling (e.g., scikit-learn, TensorFlow, PyTorch, MLflow, Kubeflow, SageMaker, Vertex AI, or equivalents).”
The ad says“Familiarity with ML frameworks and tooling (e.g., scikit-learn, TensorFlow, PyTorch, MLflow, Kubeflow, SageMaker, Vertex AI, or equivalents).”

About Gen:

Gen is a global company dedicated to powering Digital Freedom through its trusted consumer brands including Norton, Avast, LifeLock, MoneyLion and more. Our combined heritage is rooted in financial empowerment and cyber safety for the first digital generations, and today we deliver award-winning cybersecurity, online privacy, identity protection and financial wellness solutions to nearly 500 million users in more than 150 countries.

Together, we share a collective passion and vision to protect consumers and help them grow, manage and secure their digital and financial lives. We’re always looking for smart, fearless and high-impact talent who see AI as a teammate – leveraging it to move faster and deliver meaningful results.

When you’re part of Gen, you’ll have the flexibility, tools and support to do your best work and grow your career – from flexible working options and time off to competitive pay, benefits and well-being programs.

At Gen, we are scrappy and relentlessly customer driven. We create room for healthy debate, experimentation and continuous learning, and we seek out people with different experiences, identities and ideas to join our team. You’ll work with people who back each other, respect each other and understand that our differences are a competitive advantage.

If this sounds like you, we’d love you to be part of Gen.

About the Role:

The Kuala Lumpur office is the technology powerhouse of MoneyLion. We pride ourselves on innovative initiatives and thrive in a fast paced and challenging environment. Join our multicultural team of visionaries and industry rebels in disrupting the traditional finance industry!

As an MLOps Engineer, you will help design, build, and operate the next generation of our machine learning platform and infrastructure, enabling data scientists and ML engineers to reliably take models from experimentation to production at scale. You will work closely with Data Scientists, Data Engineers, and AI/ML Engineering teams to streamline end-to-end ML workflows and improve system design and architecture for production-grade ML solutions.

Key Responsibilities:

Design, build, and maintain ML infrastructure and tooling to support the full ML lifecycle (data preparation, training, evaluation, deployment, monitoring, and retraining).
Develop and maintain CI/CD pipelines for ML models, including automated testing, validation, and safe rollout/rollback strategies.
Implement robust model deployment patterns (batch, real-time, streaming) and ensure scalability, reliability, and low-latency performance in production environments.
Build and operate monitoring and observability for ML systems (data drift, model performance, system health), and define alerting/incident response processes.
Partner with Data Scientists and ML Engineers to productize models, including feature engineering pipelines, model packaging, and environment standardization.
Collaborate with Data Engineering to integrate ML workloads into data platforms and pipelines, ensuring data quality, lineage, and governance.
Drive best practices in MLOps, including versioning (data, model, code), experiment tracking, reproducibility, and documentation.
Contribute to security, compliance, and cost optimization aspects of ML infrastructure (access control, secrets management, resource utilization).
Provide technical guidance and support to cross-functional teams on ML platform usage, tools, and workflows.

About You:

Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or related field, or equivalent practical experience.
Solid programming skills in Python (preferred) or similar languages, with experience building production-grade services and tools for data/ML workflows.
Hands-on experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization/orchestration technologies such as Docker and Kubernetes.
Experience implementing CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Jenkins, Azure DevOps) for data or ML projects.
Familiarity with ML frameworks and tooling (e.g., scikit-learn, TensorFlow, PyTorch, MLflow, Kubeflow, SageMaker, Vertex AI, or equivalents).
Strong understanding of software engineering best practices: code reviews, testing, logging, monitoring, and documentation.
Good collaboration and communication skills, with experience working in cross-functional teams (Data Science, Data Engineering, Product, and Operations).

Preferred experience in a dedicated MLOps / ML Platform / ML Infrastructure role across multiple model lifecycles (from prototype to large-scale production).
Preferred experience building or supporting feature stores, model registries, and experiment tracking systems.
Preferred exposure to streaming and near-real-time data processing (e.g., Kafka, Kinesis, Pub/Sub).
Knowledge of data governance, privacy, and security considerations in ML systems.
Preferred experience with observability stacks (e.g., Prometheus, Grafana, ELK/EFK, Datadog) and setting up model and data quality monitors.
Familiarity with LLM / GenAI workloads and associated tooling is a plus (even though the core MLOps role is not GenAI/LLM-specific).

What’s Next:

Online Technical Assessment
TA Screening Call

Live Technical Assessment

Final Interview- Hiring Manager (Virtual or face-to-face)

__________

Gen is an equal opportunity employer, and we’re committed to fair, inclusive practices at every stage of the candidate and employee journey. Employment decisions are based on merit, experience and business needs.

Asks for

Values