Devops Engineer (Strong in Java & Python)

Luxoft Singapore; Singapore; On-site; SGD 150,000 - 210,000; Full time

Job summary

Luxoft Singapore is seeking an experienced AI DevOps Engineer to design, build, and maintain CI/CD pipelines for AI/ML deployments. You will manage Kubernetes/OpenShift platforms, containerise apps with Docker, and automate model lifecycle in a secure, regulated banking environment.

You will collaborate with AI engineers, data scientists, security, and application teams to operationalise AI workloads across cloud and container platforms, ensuring scalable, reliable delivery and strong governance.

Qualifications

• 6–10 years in DevOps, platform or cloud engineering.
• Hands-on Jenkins, GitHub/GitLab/Bitbucket.
• Experience deploying AI/ML models from Hugging Face or similar repositories.
• Docker and Kubernetes/OpenShift proficiency.
• Linux system administration experience.
• Scripting with Bash/Python.
• CI/CD pipeline automation experience.
• IaC with Terraform or Ansible.
• Knowledge of container registries (Artifactory/Nexus).
• Experience with AWS/Azure/GCP.

Responsibilities

• Design, build, and maintain CI/CD pipelines for AI/ML deployment.
• Deploy LLMs from Hugging Face into dev, UAT, prod environments.
• Automate model packaging, versioning, testing, release.
• Manage Kubernetes/OpenShift AI platforms for hosting/inference.
• Build deployment pipelines with Jenkins, GitHub Actions, GitLab CI.
• Containerise AI apps using Docker in K8s clusters.
• Integrate AI with enterprise apps via REST APIs/microservices.
• Manage model lifecycle, monitoring, and production support.
• Apply IaC for AI platform provisioning.
• Configure model serving platforms (vLLM, Ollama, Triton).
• Monitor health with Prometheus, Grafana, ELK, Dynatrace, or Splunk.
• Optimise GPU usage and troubleshoot performance.
• Collaborate with AI engineers to productionise models.
• Ensure security, governance, and banking compliance.
• Participate in production releases and incident management.

Skills

DevOps

Platform Engineering

Cloud Engineering

Linux administration

Scripting (bash/python)

CI/CD automation

Communication

Stakeholder management

Education

Bachelor's degree in CS/Engineering

Tools

Jenkins

GitHub Actions

GitLab CI

Docker

Kubernetes/OpenShift

Terraform/Ansible

REST APIs

Prometheus/Grafana

vLLM/Ollama/Triton

Job description

Project Description:

Our client is a leading Singapore-based financial institution embarking on an enterprise-wide AI transformation programme to accelerate the adoption of Generative AI across business and technology functions. The programme focuses on deploying and operating Large Language Models (LLMs), AI agents, and intelligent automation solutions within a secure, highly regulated banking environment.

As part of this strategic initiative, we are looking for an experienced AI DevOps Engineer / MLOps Engineer to build, automate, and manage AI model deployment pipelines, enabling secure, scalable, and reliable delivery of AI solutions across the bank.

You will work closely with AI Engineers, Data Scientists, Platform Engineers, Security teams, and Application Development teams to operationalise AI workloads on modern cloud and container platforms.

Responsibilities:

• Design, build, and maintain CI/CD pipelines for AI/ML model deployment.


• Deploy Large Language Models (LLMs) from Hugging Face and other model repositories into development, UAT, and production environments.


• Automate model packaging, versioning, testing, and release using DevOps best practices.


• Manage Kubernetes/OpenShift based AI platforms for model hosting and inference.


• Build deployment pipelines using Jenkins, GitHub Actions, GitLab CI, or similar CI/CD tools.


• Containerise AI applications using Docker and deploy them into Kubernetes clusters.


• Integrate AI solutions with enterprise applications using REST APIs and microservices.


• Manage model lifecycle, rollback strategies, monitoring, and production support.


• Implement Infrastructure as Code (Terraform/Ansible) for AI platform provisioning.


• Configure model serving platforms such as vLLM, Ollama, Triton Inference Server, or similar.


• Monitor application and model health using Prometheus, Grafana, ELK, Dynatrace, or Splunk.


• Optimise GPU resource utilisation and troubleshoot model performance issues.


• Collaborate with AI Engineers to productionise new models and inference pipelines.


• Ensure security, governance, and compliance requirements are met within the banking environment.


• Participate in production releases, incident management, and continuous platform improvements.

Mandatory Skills Description:

• 6–10 years of experience in DevOps, Platform Engineering, or Cloud Engineering.


• Strong hands-on experience with Jenkins.


• Strong knowledge of GitHub, GitHub Actions, GitLab, or Bitbucket.


• Experience deploying AI/ML models from Hugging Face or similar model repositories.


• Strong Docker and Kubernetes/OpenShift experience.


• Experience with Linux system administration.


• Hands-on scripting experience using Bash, Python, or Shell scripting.


• Experience with CI/CD pipeline automation.


• Experience with Infrastructure as Code using Terraform or Ansible.


• Knowledge of container registries such as Artifactory, Nexus, or Docker Registry.


• Experience working with cloud platforms such as AWS, Azure, or GCP.


• Understanding of GPU-enabled environments and NVIDIA CUDA ecosystem.


• Experience configuring model serving platforms such as:


• vLLM


• Ollama


• NVIDIA Triton Inference Server


• Text Generation Inference (TGI)


• Knowledge of REST APIs and microservices architecture.


• Experience with monitoring and logging tools including Prometheus, Grafana, ELK, Splunk, or Dynatrace.


• Strong troubleshooting and production support skills.


• Excellent communication and stakeholder management skills.

Nice-to-Have Skills Description:

• Experience with LangChain, LangGraph, or AI Agent frameworks.


• Experience with Vector Databases (Pinecone, Milvus, Weaviate, OpenSearch, pgvector).


• Knowledge of Retrieval-Augmented Generation (RAG) architecture.


• Experience deploying Llama, Mistral, DeepSeek, Qwen, or other open-source LLMs.


• Experience with MLflow, Kubeflow, or other MLOps platforms.


• Familiarity with Kafka or RabbitMQ.


• Experience with API Gateway and service mesh technologies.


• Knowledge of security scanning tools such as Trivy, SonarQube, Checkmarx, or Snyk.


• Experience implementing DevSecOps practices.


• Agile/Scrum delivery experience.


• Previous experience within Banking, Capital Markets, or Financial Services.


• Experience supporting AI platforms in production environments.