Senior Machine Learning Engineer

Ensign InfoSecurity; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Ensign InfoSecurity in Singapore is seeking an experienced Platform Engineer to own the design, development, and operation of our in‑house AIOps/ML/LLM platform. You will shape platform capabilities across cloud and on‑prem Kubernetes, and translate security and compliance requirements into scalable solutions.

You will build production ML/LLM workflows, manage retraining and deployment, troubleshoot across Linux, Kubernetes, and ML serving layers, and collaborate with AI, deployment, and

Qualifications

• Strong software/platform engineering fundamentals, including system design and reliable code.
• Practical understanding of the ML/LLM lifecycle: data pipelines, training, deployment, monitoring.
• Proficiency in Python, with working knowledge of Go and C++ for reading and maintaining production code.
• Solid Linux, networking, and Kubernetes troubleshooting and operations.
• Experience deploying production platforms on AWS, Azure, GCP, or on‑prem environments.
• Experience building CI/CD and MLOps workflows for production ML/LLM systems.

Responsibilities

• Own the design, development, maintenance, and evolution of the in‑house AIOps/ML/LLM platform.
• Translate client, security, compliance, and internal requirements into practical platform designs with cross‑functional teams.
• Build and operate production ML/LLM workflows, including retraining, deployment, inference serving, monitoring, rollback, and optimisation.
• Troubleshoot production issues across application, infrastructure, networking, Linux, Kubernetes, and ML serving layers.

Skills

System design

Distributed systems

API design

Observability

Authn/Authz

Testing

Maintainable code

Tools

MLflow

Kubeflow

vLLM

TensorRT

TGI

Cloud & On‑prem Kubernetes

Job description

• Own the design, development, maintenance, and evolution of the in-house AIOps / ML / LLM platform, including related cloud and on-premise Kubernetes solutions.


• Translate client, security, compliance, and internal requirements into practical platform designs with cross-functional teams.


• Build and operate production ML / LLM workflows, including retraining, deployment, inference serving, monitoring, rollback, and optimisation.


• Troubleshoot production issues across application, infrastructure, networking, Linux, Kubernetes, and ML serving layers.

Qualifications / Requirements

• Strong software/platform engineering fundamentals, including system design, API design, distributed systems, scalability, reliability, observability, authentication/authorization, testing, and maintainable code design.


• Practical understanding of the ML / LLM lifecycle, including data pipelines, model training/retraining, evaluation, experiment tracking, deployment, monitoring, and production feedback loops.


• Strong development experience in Python, with working proficiency in Go and C++ for reading, debugging, maintaining, and extending existing production codebases.


• Strong Linux, networking, and Kubernetes fundamentals, including production troubleshooting, service connectivity, ingress, resource limits, workload debugging, and deployment operations.


• Experience designing, deploying, and operating production platforms on AWS, Azure, GCP, or on-premise environments.


• Experience building CI/CD, automation, and MLOps / LLMOps workflows for production ML / LLM systems.


• Strong communication skills and ability to work with AI, deployment, infrastructure, and security teams.

Good to Have

• Deep experience operating Kubernetes in bare-metal, air-gapped, or restricted on-premise environments.


• Experience with MLflow, Kubeflow, vLLM, TensorRT, TGI, or similar ML / LLM platform tools.


• Exposure to TypeScript / React or Java-based services.