ML Engineer: Production Tools & Scalable Systems

Faculty; Melbourne; In loco; AUD 110.000 - 180.000; Tempo pieno

Descrizione del lavoro

Faculty is a human-centric AI company founded in 2014, helping over 350 global customers transform performance with responsible AI. We build production ML software and deploy scalable solutions.

This Melbourne-based role partners with engineers, data scientists and commercial leads to drive architectural decisions, define standards for deploying ML at scale, and translate complex ML concepts for stakeholders.

Competenze

• Experience operationalisingML models with Scikit-learn, TensorFlow, or PyTorch.
• Strong Python skills and software engineering practices.
• Hands-on experience with cloud platforms (AWS/Azure/GCP) and infrastructure.
• Experience with Docker and Kubernetes for scalable applications.
• Solid understanding of ML concepts (probability, statistics, learning techniques).
• Excellent communicator able to guide technical teams and explain to non-technical stakeholders.
• Thrives in a fast-paced environment with autonomous ownership.

Mansioni

• Build and deploy production-grade ML software, tools, and infrastructure.
• Create reusable, scalable solutions that accelerate ML delivery.
• Collaborate with engineers, data scientists, and commercial leads to solve client challenges.
• Lead technical scoping and architectural decisions for project feasibility.
• Define and implement Faculty's standards for deploying ML at scale.
• Advise customers and partners, translating ML concepts for stakeholders.

Conoscenze

Python

ML lifecycle

Cloud platforms

Docker/Kubernetes

ML frameworks

Communication

Strumenti

AWS

Azure

GCP

Descrizione del lavoro

Faculty is a human-centric AI company founded in 2014, helping over 350 global customers transform performance with responsible AI. We build production ML software and deploy scalable solutions.

This Melbourne-based role partners with engineers, data scientists and commercial leads to drive architectural decisions, define standards for deploying ML at scale, and translate complex ML concepts for stakeholders.