Senior Machine Learning Engineer

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

Job summary

Ensign InfoSecurity seeks an experienced Platform Engineer to own the in‑house AIOps/ML/LLM platform. You will collaborate with security, compliance, and infra teams to translate requirements into scalable platform designs and production ML/LLM workflows.

The role covers deployment, monitoring, retraining, and troubleshooting across cloud/on‑prem Kubernetes environments, with strong emphasis on reliability and security.

Qualifications

• Strong software/platform engineering fundamentals (design, APIs, distributed systems).
• ML/LLM lifecycle understanding (data pipelines, training, deployment).
• Proficient in Python; reading knowledge of Go and C++ for existing code.
• Solid Linux, networking, and Kubernetes knowledge with production troubleshooting.
• Experience deploying production platforms on cloud or on‑premises.
• Experience building CI/CD and MLOps/LLMOps workflows.

Responsibilities

• Own the design, development, maintenance, and evolution of the in-house AIOps / ML / LLM platform, including 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.

Skills

Python development

Go reading

C++ reading

Linux fundamentals

Kubernetes fundamentals

API design

Distributed systems

MLOps / LLMOps

CI/CD

Authentication/Authorization

Tools

MLflow

Kubeflow

vLLM

TensorRT

TGI

TypeScript / React

Java-based services

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.