Senior Machine Learning Engineer (PROJ-4802)

Key duties and responsibilities

The Machine Learning Engineer will be responsible for designing, developing, deploying and supporting enterprise AI, machine learning and automation solutions that deliver measurable business outcomes. Working within a multidisciplinary team, they will progress AI initiatives from proof of concept through to production and operational use. Success in the role will also involve strengthening IP Australia’s AI capability through the application of appropriate technologies and best practices, effective knowledge transfer and high-quality documentation. Key responsibilities include but are not limited to:

AI & Automation:

• Design and deliver AI & Automation components both autonomously and in collaboration with software and machine learning engineers.

• Develop, deploy, and maintain production capabilities as part of a multi-disciplinary team.

• Proactively contributes to Proofs of Concept, experimentation, problem solving and documenting approach/outcomes.

Emerging Technologies:

• Contribute to the application of emerging technologies and innovative approaches to support the agency's vision.

• Deliver solutions using the AWS platform, with a strong emphasis on serverless applications.

Communication:

• Maintain excellent communication skills to effectively collaborate with team members and stakeholders.

• Contribute to written PoC reports, evaluation/recommendation papers and technical documentation.

• Documenting and delivering detailed technical documentation to the relevant stakeholders in a timely manner ensuring actionable guidance.

• The Specified personnel shall be responsible for ensuring effective knowledge transfer to IPA APS personnel.

Success in the role will be measured by:

• Successful exploration and delivery of AI solutions that address agreed business priorities and deliver measurable organisational outcomes.

• Demonstrated uplift in organisational AI capability through use of appropriate technology, knowledge transfer, adhering to best practices and high-quality documentation.

Essential criteria

• Extensive experience in AI/ML technologies and proven ability to deliver successful Machine Learning models and AI solutions using techniques and technologies such as Natural Language Processing, PyTorch and Transformer-based models, computer vision, and classical ML techniques.

• Proven experience in MLOps product lifecycle from inception to production

• Experience with serverless delivery products

• Relevant ICT qualifications and certifications.

• Demonstrated ethical conduct, professional and other standards that your organisation would apply to the Services and the measures your organisation proposes to ensure that standards are maintained for the term of the Contract.

Desirable criteria

• Infrastructure-as-Code (preferably AWS CDK)

• AWS (particularly Serverless application delivery)

• Effective communication skills with ability to explain or translate complex models and findings to business stakeholders.

Experience with the following technologies is essential:

• Python

• PyTorch and Transformer-based models

• Natural Language Processing (e.g. NLTK, LLMs)

• Data preparation and classical ML (e.g. SQL, Pandas, scikit-learn)

• Experiment tracking and MLOps (e.g. MLflow)

• Computer vision models (e.g. CNNs, VLMs)

• Relevant ICT qualifications and/or industry certifications, and experience in the MLOps product lifecycle from inception to production, are also required.

Experience with the following technologies would be highly desirable:

• Infrastructure-as-Code (preferably AWS CDK)

• Serverless application delivery, specifically AWS