MLOps Engineer — AI Production & CI/CD (Sydney)

EML; Sydney; Hybrid; AUD 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid work options

Fitness Passport

Discounts at 350+ retailers viaRewards

Corporate wellbeing program

Comprehensive learning and development

Employee Assistance Program

Company-wide celebration events

Annual incentive scheme

Job summary

EML invites applications for an MLOps Engineer to join our Sydney team on a full-time, 12-month fixed-term contract. You will bridge ML development and production, designing scalable infrastructure and pipelines to deliver AI solutions.

You will deploy and optimise LLM workloads, build containerised services, and collaborate with Technology, AI and business stakeholders to drive reliable, production-ready AI across the business.

Qualifications

• 2+ years of experience as a MLOps engineer or Software Engineer.
• Strong Python development skills and production-ready RESTful APIs.
• Experience with CI/CD using GitHub Actions and containerised deployments.
• Experience deploying LLMs and AI workloads in production.
• Familiar with Linux, Bash scripting and automation workflows.

Responsibilities

• Design, develop and maintain scalable Python apps and AI solutions.
• Build and optimise CI/CD pipelines with GitHub Actions.
• Deploy LLM inference workloads using vLLM and Ray.
• Develop containerised apps with Docker and Kubernetes.
• Apply prompt engineering and evaluation to improve AI outputs.
• Collaborate with stakeholders to deliver production-ready AI solutions.

Skills

Python

FastAPI

GitHub Actions

Docker

Kubernetes

Linux

Bash

LLM deployment

vLLM

Ray

Prompt engineering

Tools

FastAPI

GitHub Actions

Docker

Kubernetes

vLLM

Ray

Job description

EML invites applications for an MLOps Engineer to join our Sydney team on a full-time, 12-month fixed-term contract. You will bridge ML development and production, designing scalable infrastructure and pipelines to deliver AI solutions.

You will deploy and optimise LLM workloads, build containerised services, and collaborate with Technology, AI and business stakeholders to drive reliable, production-ready AI across the business.