Senior ML/AI Engineer, MLOps on Databricks/AWS (2y)

Activate Interactive Pte Ltd.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Benefits offered by this job

Competitive compensation

Growth opportunities

2 year contract

Job summary

Activate Interactive Pte Ltd in Singapore seeks a Senior ML/AI Engineer to productionise and operate ML workloads on the SWDA data platform using AWS and Databricks. You will collaborate with data scientists, data engineers and governance stakeholders to build reliable data pipelines and reusable MLOps capabilities for secure, observable AI services from ingestion to retraining.

The role requires strong Python engineering, Databricks, MLflow, Spark/PySpark and SQL skills, plus hands-on AWS data

Qualifications

• Strong Python and software-engineering capability, including APIs, packaging, automated testing, Git, code review, debugging and modular design.

Responsibilities

• Productionising, operating and improving ML/AI workloads on the SWDA data platform using AWS and Databricks.
• Design and implement reusable platform components and golden paths for experiment tracking and model registry.
• Build and maintain data pipelines for structured and unstructured data used by ML workloads.
• Optimise costs and performance of Spark/SQL jobs and deployments.
• Implement CI/CD/CT automation across environments with governance gates.
• Provide monitoring, runbooks and incident response support for ML services.
• Support reproducibility, governance and responsible-AI practices across the pipeline.

Skills

Python

Databricks

MLflow

Spark/PySpark

SQL

AWS

CI/CD

MLOps

Kubernetes

Data engineering

Tools

Databricks Unity Catalog

Docker

Terraform

GitLab CI / GitHub Actions

Job description

Activate Interactive Pte Ltd in Singapore seeks a Senior ML/AI Engineer to productionise and operate ML workloads on the SWDA data platform using AWS and Databricks. You will collaborate with data scientists, data engineers and governance stakeholders to build reliable data pipelines and reusable MLOps capabilities for secure, observable AI services from ingestion to retraining.

The role requires strong Python engineering, Databricks, MLflow, Spark/PySpark and SQL skills, plus hands-on AWS data