Senior Machine Learning Ops Engineer

Exclaim IT; Sydney; Presencial; AUD 130.000 - 180.000; Jornada completa

Descripción de la vacante

Exclaim IT is seeking an experienced MLOps Engineer to support the development, deployment and ongoing management of machine learning solutions within a government environment. You will establish MLOps strategies, develop automated deployment pipelines and ensure secure, scalable ML systems aligned with business goals.

Key responsibilities include building CI/CD pipelines, data engineering solutions, collaboration with data scientists, and leading release planning with emphasis on testing,

Formación

• At least three years of AI/ML engineering experience with deployment focus.
• Experience implementing data solutions with SQL, Spark and data pipelines.
• Strong programming in Python and ML frameworks.
• Familiar with CI/CD, testing, monitoring, and retraining processes.
• Experience with data lakes and distributed data technologies.

Responsabilidades

• Develop and maintain automated CI/CD pipelines to test, deploy, monitor and retrain models.
• Plan and deliver data engineering solutions balancing performance, security and governance.
• Collaborate with scientists and engineers to implement ML algorithms and integrate with systems.
• Lead release planning, deployment automation and rollback procedures.
• Ensure ethical AI practices, privacy risk management and policy compliance.
• Communicate designs and progress with product managers and stakeholders.
• Monitor emerging MLOps technologies and drive continuous improvement.

Conocimientos

Python programming

SQL

Spark/PySpark

Git

Linux

Stakeholder communication

Problem solving

Educación

Advanced degree in Computer Science, Statistics, Mathematics or related field

Herramientas

MLflow

Kubeflow

OpenShift

Kubernetes

Hadoop

Hive

Impala

Kafka

Descripción del empleo

We are seeking an experienced MLOps Engineer to support the development, deployment and ongoing management of machine learning solutions within a government environment. This role will involve establishing and implementing MLOps strategies, developing automated deployment pipelines and ensuring machine learning systems are secure, reliable, scalable and aligned with business objectives.

Key responsibilities

• Develop and maintain automated CI/CD pipelines to test, deploy, monitor and retrain machine learning models.
• Plan and deliver data engineering solutions that balance functional requirements with performance, security, scalability and compliance.
• Collaborate with data scientists and engineers to implement machine learning algorithms and integrate solutions into existing systems.
• Contribute to the development, evaluation, monitoring and deployment of machine learning systems.
• Assess the suitability of machine learning approaches for business problems and datasets, providing advice on techniques and their effectiveness.
• Lead release planning, risk assessments, deployment scheduling and post-release reviews.
• Identify and implement appropriate release management, deployment automation and rollback processes.
• Plan and oversee testing activities throughout the development lifecycle, providing guidance on testing methodologies, automation and quality standards.
• Apply ethical AI practices, assess privacy risks and ensure compliance with relevant policies and industry guidelines.
• Work with product managers and business stakeholders to communicate solution designs, technical requirements and project progress.
• Monitor emerging technologies, model architectures and MLOps practices to identify opportunities for continuous improvement.

Highly desirable criteria

• Data engineering – SFIA Level 5: Demonstrated experience planning and driving data engineering solutions, balancing functional and non-functional requirements, and ensuring compliance with data standards, architectures, security and governance requirements.
• Machine learning – SFIA Level 4: Experience assessing machine learning approaches, implementing algorithms developed by data scientists, evaluating techniques and contributing to the development, monitoring and deployment of machine learning systems.
• Release and deployment – SFIA Level 5: Proven ability to lead release planning, assess deployment risks, coordinate release schedules, conduct post-release reviews and implement appropriate deployment automation and rollback procedures.
• Testing – SFIA Level 5: Demonstrated experience planning and managing testing activities across the development lifecycle, advising on testing methods and automation, monitoring quality standards, managing risks and driving continuous improvement.
• Programming and software development – SFIA Level 3: Experience designing, coding, testing, documenting, maintaining and refactoring moderately complex programs and scripts in accordance with agreed standards and tools.
• Experience with Kubernetes, preferably Red Hat OpenShift, and containerisation technologies.
• Familiarity with MLOps platforms and tools such as MLflow, Kubeflow or equivalent.
• Experience working with data lakes and distributed data technologies, including Hadoop, Hive, Impala and Kafka.
• Knowledge of ethical AI practices, privacy risk assessment and responsible machine learning implementation.

Skills and experience

• At least three years of experience in AI/ML engineering, with a demonstrated track record in machine learning operations and deployment.
• An advanced degree in Computer Science, Statistics, Mathematics or a related field, preferably with a focus on data science, machine learning or artificial intelligence.
• Strong programming skills in Python, including experience with Python APIs and machine learning frameworks.
• Experience developing batch and streaming data pipelines.
• Proficiency in SQL, Spark/PySpark and relational database management systems.
• Experience implementing data solutions using relational databases, distributed file systems and blob storage.
• Practical experience with Git, Linux, containerisation and Kubernetes.
• Familiarity with data lakes and technologies such as Hadoop, Hive, Impala and Kafka.
• A sound understanding of CI/CD, automated testing, model deployment, monitoring and retraining processes.
• Strong analytical, problem-solving and stakeholder communication skills, with the ability to translate technical concepts into practical solutions.