MLOps Engineer
Expired
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4impact
Brisbane QLD or Sydney NSW
hybridFull Time
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Posted 5 months ago
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View all AI roles →Machine Learning Engineer
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- 12‑month daily rate contract
- Build, maintain, and optimise ML pipelines across Databricks and AWS
- Brisbane or Sydney based - Hybrid role (3 days office, 2 days remote)
Position Purpose
Responsible for the full cycle of Machine Learning (ML)/AI projects in production. This includes data ingestion, transformation, cost optimisation, deployment, monitoring, and supporting Databricks infrastructure. The role ensures reliable execution of ML products in production while collaborating across data science, engineering, and DevOps teams.
Required Skills & ExperienceResponsible for the full cycle of Machine Learning (ML)/AI projects in production. This includes data ingestion, transformation, cost optimisation, deployment, monitoring, and supporting Databricks infrastructure. The role ensures reliable execution of ML products in production while collaborating across data science, engineering, and DevOps teams.
- 3+ years in a DevOps or MLOps role, ideally in AWS environments.
- Strong experience with Databricks platform and ML lifecycle management.
- Proficiency in Python, Spark, and scripting (Bash, PowerShell).
- Experience with Terraform for cloud infrastructure configuration.
- Knowledge of CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins).
- Exposure to cloud monitoring/logging tools (CloudWatch).
- Strong understanding of ML model lifecycle and productionisation.
- Ability to prioritise tasks and manage multiple projects simultaneously.
- Excellent communication skills to bridge technical and non‑technical stakeholders.
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or related quantitative field.
- Strong proficiency in Python, SQL, Spark, and scripting languages (Bash, PowerShell).
- Hands‑on experience with CI/CD tools (Azure DevOps, GitHub Actions, Jenkins).
- Familiarity with cloud monitoring/logging tools (CloudWatch, Prometheus, Grafana).
- Knowledge of ML lifecycle management, feature stores, and generative AI frameworks.
- Exposure to multi‑cloud environments (AWS, Azure, Snowflake) and modern data tools (dbt, Airflow, Kafka).
- Proven ability to work in support‑oriented environments—handling interruptions, prioritising requests, and providing reliable assistance to data scientists, engineers, and stakeholders.
- Ability to adapt to changing priorities and deliver under pressure while maintaining production reliability.