Machine Learning Engineer

Job Description

Machine Learning Engineer

\n

This range is provided by NineTech. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

\n

Base pay range

\n

A$90.00/hr - A$90.00/hr

\n

Direct message the job poster from NineTech

\n

We're urgently seeking a Senior DevOps ML Engineer with a rare combination of DevOps leadership, software engineering depth, and MLOps expertise.

\n

The client requires someone with DevOps leadership, coding skills akin to a seasoned backend engineer, and ability to optimize AI systems with precision.

\n

You will be responsible for the end-to-end design, deployment, and operation of AI platforms.

\n

Expect to split your time approximately as follows: 60% DevOps/AI infrastructure, 30% backend coding, and 10% MLOps/model lifecycle tasks.

\n

One week, you may be helping data scientists productionize LLM pipelines. The next, you're deep in Terraform, Helm charts, and CUDA kernels—tuning real-time latency for a Tokkio-powered digital avatar.

\n

Key Responsibilities

\n

AI Platform & DevOps (60%)

\n

\n
• Architect, deploy, and maintain GPU-accelerated Kubernetes clusters using Helm, NGC containers, and custom K8s operators.
\n
• Build and maintain CI/CD pipelines (GitHub Actions, Jenkins, Argo CD) to enable continuous delivery of both software and models.
\n
• Automate infrastructure across AWS, Azure, and on-prem environments using Terraform or Pulumi.
\n
• Optimize GPU workloads and ensure reliability across hybrid or multi-cloud AI platforms.
\n

\n

Model Lifecycle & MLOps

\n

\n
• Collaborate with data scientists to containerize, benchmark, and tune LLMs, diffusion models, and multimodal pipelines.
\n
• Implement data governance and tracking for AI data pipelines (e.g., LakeFS, Feast).
\n
• Maintain feature and vector stores, ensuring reproducibility and performance of AI applications.
\n

\n

Hands-on Engineering

\n

\n
• Develop backend services and APIs in Python and C++ (CUDA, Triton, TensorRT-LLM) and optionally in TypeScript.
\n
• Integrate components from the client's digital human ecosystem: Riva (speech), Tokkio, Maxine (Audio2Face, eye contact), Omniverse.
\n
• Build reusable SDKs, CLI tools, and internal libraries to accelerate AI/ML workflows across teams.
\n

\n

Required Qualifications

\n

\n
• 10+ years of experience building and operating production-grade software systems.
\n
• 2+ years focused specifically on AI/ML platforms or infrastructure.
\n
• Proven expertise in CI/CD, GitOps, Terraform, and Helm.
\n
• Strong Kubernetes and Docker experience, including GPU workload scheduling.
\n
• Advanced Linux administration skills and experience profiling GPU workloads.
\n
• Expert-level Python plus one systems language (C++, Go, Rust, or Java).
\n

\n

Seniority level

\n

\n
• Mid-Senior level
\n

\n

Employment type

\n

\n
• Contract
\n

\n

Job function

\n

\n
• Information Technology
\n

\n

Industries

\n

\n
• IT Services and IT Consulting
\n