MLOps Engineer (Senior/Associate Principal), AI Devt Office (AIDO)

Singapore General Hospital; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Singapore General Hospital Pte Ltd. is seeking a mid-senior MLOps Engineer to join the AI Development Office in Singapore. You will help build the MLOps platform, define deployment standards, and scale clinical AI projects into production.

You will work with tech leads to shape tooling choices, CI/CD pipelines, and observability while ensuring compliance in a regulated healthcare setting.

Qualifications

• STEM Bachelor degree with 3–5 years of experience in MLOps, DevOps, software engineering, or cloud infrastructure roles.
• Hands-on experience with AWS services — EKS, ECR, S3, IAM, CloudWatch.
• Proficiency in Python for scripting, automation, and service development.
• Working experience with Docker and Kubernetes in real-world deployment environments.
• Experience with building or maintaining CI/CD pipelines — GitHub Actions, Jenkins, GitLab CI, or equivalent.
• Familiarity with ML lifecycle tooling such as MLflow for experiment tracking and model versioning.
• Familiarity with AI/ML platform concepts — model registries, RAG pipelines, and LLM frameworks such as LangChain or LlamaIndex.
• Basic understanding of model monitoring and observability practices.
• Comfortable working in regulated or on-premise environments — not exclusively cloud-native.
• Ability to translate ambiguous requirements into clear technical plans and execute with minimal direction.
• Curious and self-directed learner — comfortable picking up new tools and technologies without a formal training path.
• Good communication skills — able to work alongside data scientists, engineers, IT professionals and non-technical stakeholders.

Responsibilities

• Platform & Infrastructure: Design, build-out and maintain MLOps platform; contribute to tooling choices, deployment standards and infrastructure decisions.
• CI/CD pipelines: Build and maintain pipelines for model training, validation, containerisation, and deployment.
• Observability: Set up monitoring, alerting and observability for model performance, data drift, latency and infra health.
• Documentation: Maintain clear docs for infrastructure and deployment processes.
• Cloud Strategy & Architecture: Deploy clinical AI products to cloud infrastructure including AWS; configure cloud-native tooling for AI workloads in regulated environments.
• Hybrid/cloud-ready infra: Support progression toward hybrid and cloud-ready infrastructure.
• Engineering Standards: Establish engineering best practices; code reviews, branching strategies, deployment checklists.
• Model packaging: Collaborate with data scientists to package models as production-ready services (REST APIs, batch inference).
• Requirement translation: Turn ambiguous requirements into clear technical plans and execute with minimal direction.

Skills

Python

Communication

Self-directed learner

Education

STEM Bachelor degree

Post-graduate degree advantageous

Tools

Docker

Kubernetes

GitHub Actions

Jenkins

GitLab CI

MLflow

Prefect

Airflow

LangChain

LlamaIndex

Prometheus

Grafana

OpenShift

Nginx

AWS (EKS/ECR/S3/IAM/CloudWatch)

Job description

JOB OVERVIEW

We are looking for a mid-senior MLOps Engineer to join the Artificial Intelligence Development Office - Singapore General Hospital's in-house AI team. AIDO functions as an end-to-end product team, building and deploying AI solutions that directly support clinical decision-making.

You will work closely with our technical leads to build out MLOps and platform engineering functions from the ground up. You will be involved in tooling decisions, pipeline setup, deployment workflows, and engineering standards as we scale our clinical AI projects into production.

KEY RESPONSIBILITIES

Platform & Infrastructure

• Support the design and build-out of AIDO's MLOps platform - contributing to tooling choices, deployment standards, and infrastructure decisions
• Build and maintain CI/CD pipelines for model training, validation, containerisation, and deployment
• Set up and maintain monitoring, alerting, and observability solutions covering model performance, data drift, latency, and infrastructure health
• Maintain clear documentation for infrastructure and deployment processes

Cloud Strategy & Architecture

• Support and drive the deployment of clinical AI products to cloud infrastructure including AWS
• Implement and configure cloud-native tooling suitable for AI workloads in a regulated healthcare environment
• Support the team's progression toward hybrid and cloud-ready infrastructure

Engineering Standards & Team Enablement

• Help the department to establish and adopt engineering best practices - code review processes, branching strategies, deployment checklists, etc.
• Work with data scientists to package models as reproducible, production-ready services (REST APIs, batch inference)
• Translate ambiguous requirements into clear technical plans and execute with minimal direction and approach problems systematically

REQUIRED SKILLS & EXPERIENCE

• STEM Bachelor degree with 3-5 years of experience in MLOps, DevOps, software engineering, or cloud infrastructure roles. Relevant post-graduate degree would be an advantage.
• Hands-on experience with AWS services — EKS, ECR, S3, IAM, CloudWatch
• Proficiency in Python for scripting, automation, and service development
• Working experience with Docker and Kubernetes in real-world deployment environments
• Experience with building or maintaining CI/CD pipelines — GitHub Actions, Jenkins, GitLab CI, or equivalent
• Familiarity with ML lifecycle tooling such as MLflow for experiment tracking and model versioning
• Familiarity with AI/ML platform concepts — model registries, RAG pipelines, and LLM frameworks such as LangChain or LlamaIndex
• Basic understanding of model monitoring and observability practices
• Comfortable working in highly regulated or on-premise environments — not exclusively cloud-native
• Ability to translate ambiguous requirements into clear technical plans and execute with minimal direction
• Curious and self-directed learner — comfortable picking up new tools and technologies without a formal training path
• Good communication skills — able to work alongside data scientists, engineers, IT professionals and non-technical stakeholders

PREFERRED QUALIFICATIONS

• Exposure to LLM serving infrastructure such as vLLM
• Background in healthcare, government, or regulated environments
• Familiarity with Prefect, Airflow, or similar orchestration tools
• Experience with Prometheus, Grafana, or ELK stack
• Experience with OpenShift or Red Hat environments
• Prior experience at GovTech, AISG, A*STAR, DSO, or similar Singapore public sector organisations
• AWS certification is a plus — DevOps Engineer, Machine Learning Specialty, or Solutions Architect Associate

TECHNICAL ENVIRONMENT

Python, FastAPI, Docker, Kubernetes, AWS (EKS / ECR / S3 / IAM / CloudWatch), MLflow, Prefect / Airflow, vLLM, CI/CD (GitHub Actions / Jenkins / GitLab CI), Prometheus, Grafana, OpenShift / Red Hat AI, Nginx