Job Description

We are looking for a practical, production-minded ML Ops Engineer to own the data, infrastructure and operational pipelines that support our machine learning systems, with particular ownership of the production ML lifecycle for computer vision.

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This is an end-to-end ownership role: from image, video and related data ingestion and dataset creation through training infrastructure, deployment, monitoring, retraining and production reliability.

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The Opportunity

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You will build and operate the platform that enables computer vision models to move from experimentation into reliable production use. You will:

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- design and operate scalable data ingestion, transformation and processing pipelines
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- build robust dataset creation, validation, labelling and versioning workflows
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- create reproducible training, evaluation and experimentation pipelines
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- deploy, version and roll back models safely across live environments
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- establish monitoring, alerting, drift detection and automated retraining loops
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You will work closely with ML and computer vision engineers, data engineers, software engineers and domain experts, while remaining accountable for the reliability and performance of the end-to-end ML lifecycle.

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What You Will Own

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This role sits at the intersection of ML engineering, data engineering, platform engineering and production operations.

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Your ownership will include:

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- data ingestion, storage, lineage, quality and lifecycle management
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- workflow orchestration for labelling, training, evaluation and inference
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- reproducible environments, experiment tracking and model registries
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- deployment pipelines,



release controls and workplace management
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- system observability, cost, latency, throughput and reliability
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- production feedback loops, incident response and continuous improvement
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You will have the autonomy to choose the right tools, simplify brittle processes and build the operational foundations that allow the wider team to ship with confidence.

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What We Are Looking For

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Ideally, you will have 3-5 years of experience building and operating ML or data systems in commercial or other real-world production environments. We are looking for evidence of systems used by real operators or customers, rather than experience gained primarily through academic research.

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You are comfortable working with:

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- large, imperfect and continuously changing datasets
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- cloud infrastructure, containers and infrastructure as code
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- ambiguous requirements and fast iteration cycles
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- operationally critical systems where failures must be visible and recoverable
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You move quickly, make pragmatic trade-offs and take responsibility for outcomes. You are likely stronger at building dependable systems than presenting elaborate architecture.

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Technical Background

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You will probably have strong experience across several of:

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- Python,



SQL and production software engineering practices
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- workflow orchestration and distributed data processing
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- ML lifecycle tooling, experiment tracking and model registries
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- containerisation and orchestration, including Docker and Kubernetes
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- cloud platforms, infrastructure as code and CI/CD for ML systems
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- object storage, dataset versioning, data validation and lineage
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- monitoring, observability, alerting and model or data drift detection
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- exposure to computer vision, video processing or GPU-based ML workloads
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experience with computer vision - particularly moving-object video, tracking, temporal context or trajectory analysis - is a bonus, as is a strong interest in developing in this area. The primary requirement is ownership of production ML and data pipelines.

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We care more about judgement, execution speed and production ownership than academic prestige or a specific technology stack.

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What Success Looks Like

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Within the first few months, we would expect:

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- reproducible training and evaluation workflows with clear dataset and model lineage
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- a dependable model release process with testing, versioning and rollback controls
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- monitoring and alerting that identify data, model and system issues early
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- clear ownership, runbooks and automated recovery or retraining processes
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Success in this role is measured by the speed, reliability and repeatability of the full ML lifecycle - and by how confidently the team can move models into production.

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Compensation

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Compensation is flexible and designed to attract exceptional individuals.

📌 ML Ops Engineer (Queensland)
🏢 seek.com.au
📍 Queensland