Senior Engineer/Principal Engineer for AI Engineering

DSTA - Defence Science & Technology Agency; Singapore; On-site; SGD 50,000 - 80,000; Full time

Job summary

A government agency in Singapore is seeking an experienced MLOps / MLSecOps Engineer to join their AI engineering team. In this full-time role, you will be responsible for ensuring the security, reliability, and governance of ML pipelines and deployed AI systems. Candidates should have at least one year of MLOps experience, a degree in Computer Science, and proficiency in Python alongside other programming languages. Familiarity with Kubernetes, Docker, and various ML tools is preferred.

Qualifications

• At least one year of experience in MLOps/MLSecOps.
• Hands-on experience with ML lifecycle tools.
• Programming experience in one language apart from Python.

Responsibilities

• Design and implement state of the art Agentic AI architectures.
• Implement secure ML pipelines from data ingestion to model deployment.
• Automate training, testing, and deployment processes.

Skills

MLOps

MLSecOps

Python

Containerisation tools

CI/CD tools

Education

Bachelor’s or Master’s in Computer Science or related field

Tools

Kubernetes

Docker

MLflow

ClearML

Job description

Overview

We are seeking an experienced, hands-on MLOps / MLSecOps Engineer to join our AI engineering team. This role bridges machine learning, operations, and security by ensuring that our ML pipelines and deployed AI systems are secure, resilient, and trustworthy. You will work closely with AI engineers, software developers, platform teams and security specialists to operationalise ML at scale with security, reliability, and governance built-in.

Responsibilities

• Next bound AI: Design and implement state of the art Agentic AI architectures
• Secure ML Lifecycle Management: Architect, implement, and manage secure ML pipelines from data ingestion to model deployment.
• MLOps / MLSecOps Integration: Automate training, testing, deployment, and monitoring with modern MLOps / MLSecOps tools.
• Security by Design: Embed security controls into ML and data workflows; ensure compliance with organisational standards.
• Vulnerability Management: Identify and mitigate risks across ML infrastructure (containers, data, and models).
• Model Security & Robustness: Safeguard against adversarial attacks and performance degradation from drift.
• Continuous Monitoring & Continuous Training: Develop observability pipelines to monitor deployed models for drift, anomalies, and performance degradation, and implement systems to support continuous retraining when models under-perform.
• Cross-Functional Collaboration: Partner with AI engineers, software developers, as well as DevSecOps and Infrastructure teams to enhance developer experience and platform capabilities.
• Governance & Compliance: Support responsible, safe, and reliable AI adoption in line with organisational and regulatory requirements.

Job Requirements

• Bachelor’s or Master’s in Computer Science or related field.
• At least one year of experience in MLOps/MLSecOps.
• Hands-on experience with
• • ML lifecycle tools (e.g. ClearML, MLflow, Kubeflow, NVIDIA Triton, vLLM)
  • Containerisation and orchestration tools (e.g. Kubernetes, Docker)
  • CI/CD tools (e.g. GitLab CI, ArgoCD)
• Programming experience in Python and one other language (e.g. Go, Rust, C++, Java).

Preferred Skills

• Familiarity with Agentic AI architectures and workflows
• Familiarity with ML optimisation techniques (e.g. quantisation, pruning, distillation).
• Familiarity with ML security threats (e.g. data poisoning, model extraction, adversarial attacks).
• Familiarity with ML monitoring & observability platforms.
• Experience with air-gapped or high-assurance environments

Experience

2–8 years

Job Type

Full-Time

Qualification

Bachelor's degree or equivalent

Working Hours

Standard Hours

Programme Centre / Entity

DIGITAL HUB