Data Science DevOps Engineer: AI/ML Production Lead

The Recruitment Hive Pty Ltd; Sydney; On-site; AUD 140,000 - 190,000; Full time

Job summary

The Recruitment Hive Pty Ltd is seeking a Data Science DevOps Engineer to lead the design and delivery of AI and data science solutions in production. The role requires technical leadership across ML, DevOps, and data engineering with a focus on scalable data pipelines and reproducible deployments.

You will coordinate specialists, standardise components, and drive production readiness from prototypes, while ensuring robust testing, monitoring and automation across cloud-native environments.

Qualifications

• Experience delivering Data Science/ML on AWS in production.
• Technical leadership across ML/AI, DevOps and data engineering.
• Proven ability to move prototypes to production-ready deployments.
• Experience coordinating specialists across ML, DevOps, data engineering and cloud teams.

Responsibilities

• Lead design and delivery of engineering approaches that enable machine learning and data science solutions.
• Provide technical leadership across AI engineering, machine learning engineering, DevOps and data engineering practices.
• Identify opportunities to standardise common technical components across multiple products and prototypes.
• Lead the development of data preparation, feature engineering, data validation and pipeline approaches that support AI and data science delivery.

Skills

AWS

MLOps

Data pipelines

DevOps

Leadership

Cloud architectures

Education

Bachelor's in CS/Engineering

Tools

Python

Docker

Kubernetes

Git

Terraform

Job description

The Recruitment Hive Pty Ltd is seeking a Data Science DevOps Engineer to lead the design and delivery of AI and data science solutions in production. The role requires technical leadership across ML, DevOps, and data engineering with a focus on scalable data pipelines and reproducible deployments.

You will coordinate specialists, standardise components, and drive production readiness from prototypes, while ensuring robust testing, monitoring and automation across cloud-native environments.