Senior Machine Learning Engineer – DevSecOps Focus | 12-Month Contract
We are seeking a Senior Machine Learning Engineer with proven experience in training AI models, secure pipeline development, and C++/Conan-based environments. This 12-month contract role offers a hybrid work arrangement and the potential for extension. You'll join a high-calibre team working at the intersection of machine learning and modern DevSecOps.

About You
To be considered for this role, you must hold Australian Citizenship and already possess Baseline Security Clearance. We are looking for a highly technical engineer who is comfortable both training ML models and applying DevSecOps best practices within complex C++ and CUDA environments.

You have a deep understanding of AI model development, MLOps workflows, and secure, scalable CI/CD infrastructure. You're confident working across both Python and C++, integrating high-performance systems with modern ML platforms like Kubernetes and Kubeflow.

Responsibilities

As a Senior Machine Learning Engineer, you will:

Design, train, and deploy AI models (e.g., using PyTorch and YOLO for computer vision tasks).

Build and maintain secure, automated pipelines using Kubernetes and Kubeflow.

Manage C++ codebases and dependencies with Conan, including CUDA-linked builds.

Develop and operationalise secure CI/CD pipelines for ML and C++ components.

Mentor engineers on MLOps practices, DevSecOps principles, and model lifecycle management.

Collaborate with stakeholders to ensure alignment between model performance and system requirements.

Required Skills & Experience

Strong experience with PyTorch, YOLO, and training deep learning models.

Solid knowledge of ML training pipelines, evaluation, and optimisation techniques.

Professional experience with C++, including Conan for dependency and package management.

Hands-on CUDA development or GPU-accelerated ML workflow experience.

Proven ability to deploy ML workloads on Kubernetes/Kubeflow.

Python fluency for data wrangling, orchestration, and ML scripting.

Expertise in DevSecOps and secure software delivery pipelines (e.g., GitLab CI/CD).

Bonus Qualifications

Experience with MLOps platforms (MLflow, Weights & Biases).

Background in secure, regulated, or government environments.

Experience with distributed training, GPU cluster orchestration, or HPC systems.

Knowledge of secure development practices and threat modelling for ML systems.

Qualifications & Clearance Requirements

Bachelor’s or Master’s Degree in Computer Science, Machine Learning, Engineering, or a related discipline.

Australian Citizenship is mandatory.

Baseline Security Clearance is a strict requirement for this role.

About Us

We are a forward-thinking technology team delivering secure, scalable, and intelligent solutions for government and enterprise clients. Our work spans advanced machine learning, high-performance systems, and DevSecOps excellence. Join a collaborative, high-impact environment where you’ll shape the AI infrastructure of tomorrow.