MLOps & AI Engineer: Production AI on Databricks & AWS

FPT Asia Pacific; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

FPT Asia Pacific in Singapore is hiring an ML/AI Engineer to productionise and optimise ML workloads on AWS and Databricks. You will build and maintain data and feature pipelines, collaborate with data scientists and governance stakeholders, and establish reusable MLOps capabilities across ingestion, experimentation, deployment, monitoring and retraining.

The role emphasizes scalable lakehouse patterns, cost-aware Spark/SQL tuning, containerised jobs, CI/CD automation, and reliable observability

Qualifications

• Degree in computer science, AI, data science or related technical field.
• 4–7 years hands-on software, data or ML engineering, production ownership preferred.

Responsibilities

• Design and implement reusable platform components for ML/Ops on Databricks and AWS.
• Develop and maintain feature engineering pipelines on Databricks and AWS.
• Optimise Spark and SQL workloads for performance and cost.
• Build automated CI/CD for code, data, features, prompts and models across environments.
• Ensure reproducibility, governance and lineage across data and models.
• Develop monitoring, alerts, runbooks and incident response processes.

Skills

Python

APIs

Git

Data structures

Problem solving

Structured thinking

Education

Bachelor's degree in CS/AI/Data Science/SE

Tools

Databricks

MLflow

Spark/PySpark

SQL

AWS

Docker

Terraform

CI/CD tools (GitLab CI, GitHub Actions, Jenkins)

Job description

FPT Asia Pacific in Singapore is hiring an ML/AI Engineer to productionise and optimise ML workloads on AWS and Databricks. You will build and maintain data and feature pipelines, collaborate with data scientists and governance stakeholders, and establish reusable MLOps capabilities across ingestion, experimentation, deployment, monitoring and retraining.

The role emphasizes scalable lakehouse patterns, cost-aware Spark/SQL tuning, containerised jobs, CI/CD automation, and reliable observability