Enterprise Analytics – Machine Learning Ops/AI Projects (VP1)

WhiteCrow Research; Kuala Lumpur; On-site; MYR 250,000 - 420,000; Full time

Job summary

WhiteCrow Research is seeking an Enterprise Analytics – Machine Learning Ops professional (VP1) to lead automation, DevOps, and AI production pipelines across cloud and on-prem environments. You will implement secure, scalable systems and collaborate with cross-functional teams to translate AI workflows into robust engineering solutions.

The role requires hands-on expertise in Linux, CI/CD, OpenShift, Docker, Kubernetes, and cloud platforms (AWS), plus a strong background in Python and AI

Qualifications

• Bachelor’s degree recognized in CS/Engineering or related field.
• Strong Bash/Unix/Linux command-line skills.
• Hands-on CI/CD experience with Bitbucket, Jenkins, Artifactory, Sonar, Veracode.
• Hands-on experience with OpenShift, Docker, Kubernetes.
• Experience with AWS and AI security/compliance in AI systems.
• Familiarity with API integration and microservices.

Responsibilities

• Developing and maintaining automation scripts using Linux shell scripting, Python, or other relevant tools.
• Developing and maintaining the entire DevOps pipelines.
• Ensuring seamless deployment and integration between cloud/prem environments (AWS).
• Integrating AI models into production environments using containerized platforms such as OpenShift.
• Implementing and maintaining network security protocols to safeguard AI systems and data pipelines.
• Collaborating with cross-functional teams to understand AI workflows and translate them into robust engineering solutions.
• Monitoring and optimizing system performance, reliability, and scalability.
• Supporting CI/CD processes and infrastructure for AI model deployment and updates.

Skills

Linux Bash

CI/CD pipelines

Python

AI/ML concepts

Cloud platforms

API & Microservices

OpenShift

Docker

Kubernetes

Security in AI

LANGFUSE/Observability

Education

Bachelor’s degree in Computer Science, Engineering, or related field

Tools

OpenShift

Docker

Kubernetes

Bitbucket

Jenkins

Artifactory

Sonar

Veracode

JIRA

Confluence

Langfuse

Elastic Stack

Grafana

OpenTelemetry

Ctrl-M

AWS

Job description

We are global talent research, insight, and sourcing specialists with offices in the UK, USA, Singapore, Malaysia, Hong Kong, Dubai, and India. Our international reach has helped us to understand and penetrate specialist markets at a global level. In addition to this, our service is also extended to complement our client’s in-house talent acquisition teams.

About our client

Our Client operates in the Financial Services Industry, with its headquarters rooted strongly in Singapore. It has its branches spread to more than 15 countries, providing employment to more than 25,000 people all over the world. They fall in the Forbes Global 2000 (2022). Their core business is to offer financial services to its clients, ranging from Investment Banking to Corporate as well as Personal Banking Services.. They are also well known for their Residential Home Loan Business.

As a Enterprise Analytics – Machine Learning Ops/AI Projects (VP1), you will be responsible for:

• Developing and maintaining automation scripts using Linux shell scripting, Python, or other relevant tools.
• Developing and maintaining the entire DevOps pipelines.
• Ensuring seamless deployment and integration between cloud/prem environments (AWS).
• Integrating AI models into production environments using containerized platforms such as OpenShift.
• Implementing and maintaining network security protocols to safeguard AI systems and data pipelines.
• Collaborating with cross-functional teams to understand AI workflows and translate them into robust engineering solutions.
• Monitoring and optimizing system performance, reliability, and scalability.
• Supporting CI/CD processes and infrastructure for AI model deployment and updates.

What you already have...

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• Bash and Unix/Linux command-line toolkit is a must-have.
• Deep technical background, with hands-on experience in supporting CI/CD pipelines (Bitbucket, Jenkins, Artifactory, Sonar, Veracode for SAST, JIRA workflow, and Confluence).
• Hands-on experience with OpenShift, Docker, Kubernetes.
• Knowledge of cloud platforms (e.g. AWS) is a must-have.
• Exposure to data and network security and compliance in AI systems.
• Knowledge of API integration and microservices architecture.
• Proficiency in Python used both for automation and ML-related tasks
• Knowledge of Workflow Orchestrator, such as Ctrl-M
• Good knowledge of Logging and Monitoring tools, such as Splunk and Geneos.
• Experience with Observability framework, such as Langfuse, Elastic Stack, Grafana, Open Telemetry.
• Understanding of Generative AI (e.g. prompt engineering, RAG pipelines) and Agentic AI concepts.