ML Platform Engineer (AU)

About the role

DroneShield is seeking an ML Platform Engineer with relevant experience to join the team in Sydney, Australia.

The position will report to the Team Lead, Data Platform Engineering.

The primary focus of the role is to consolidate, standardise, and evolve DroneShield’s existing AI/ML pipelines and tooling into a reliable, scalable internal platform used by multiple engineering teams.

Responsibilities, Duties and Expectations 

• Own and evolve the end‑to‑end AI/ML platform used across DroneShield, spanning data ingestion, annotation, training, evaluation, tuning, and deployment.
• Build and maintain self‑service tooling, templates, and CI/CD pipelines that enable teams to safely take models from research to production.
• Collaborate closely with AI/ML, Data Engineering, and Infrastructure teams to align platform capabilities with real‑world operational needs.
• Assess and uplift existing ML pipelines, tools, and workflows to improve reliability, scalability, and reuse across teams.
• Establish best‑practice approaches for model versioning, experiment tracking, provenance, and reproducibility.
• Develop and support model serving infrastructure (batch and/or online), including rollout strategies.
• Implement observability and monitoring for ML systems, including model performance, data quality, drift detection, and operational health.
• Drive improvements in developer experience, including documentation, examples, onboarding guides, and internal support.
• Assist software & hardware development teams to make informed decisions about the future direction of complex deployed system design.
• Contribute to the long‑term evolution of DroneShield’s ML platform architecture, with a focus on reliability, security, scalability, and operational excellence.

Qualifications, Experience and Skills 

• Bachelor’s degree (or higher) in Computer Science, Software Engineering, Data Engineering, or a related technical field, or equivalent practical experience.
• Strong proficiency in Python; experience with Go or another compiled language is highly desirable.
• Solid understanding of software architecture, API design, and system reliability in production environments.
• Experience working in multi‑disciplinary environments, collaborating effectively with data scientists, ML engineers, platform engineers, and domain experts.
• 3+ years of experience building and operating production software systems, with significant exposure to machine learning or data‑intensive platforms.
• Hands‑on experience designing or maintaining ML infrastructure or platforms.
• Practical understanding of the ML lifecycle, including data curation, annotation workflows, model training, validation, deployment, and retraining.
• Experience with containerised and cloud‑native infrastructure, including Docker, Kubernetes, and CI/CD systems.
• Familiarity with monitoring and observability for ML systems (e.g. performance metrics, data drift, model degradation).

Working knowledge of:

• Version control and code review practices.
• Automated testing strategies.
• Infrastructure‑as‑code or configuration‑driven systems.

Who you are

• You are a strong individual contributor who takes ownership of complex technical problems end to end.
• You influence others through engineering excellence, helping teams adopt better practices via code, tooling, and collaboration.
• You stay current with modern best practices in your areas of expertise and apply them pragmatically.
• You are motivated by building scalable systems that create real leverage for the business.
• You are driven to achieve at the highest technical bar to grow the company and compete with the big players in the industry.

Note for recruitment agencies: We do not accept unsolicited candidates from external recruiters unless specifically instructed.