Senior AI/Machine Learning Engineer

Overview

An exciting opportunity exists for a Machine Learning Engineer with strong experience in applied machine learning and production delivery within our Data and Technology Group (DTG). As part of the AI Factory team, you will contribute to the application of cutting-edge technologies and innovative approaches to help further our organisation's vision through the delivery of AI solutions.

The engagement will support the AI Factory within the DTG. The successful candidate will contribute to the design, development, evaluation and operationalisation of AI capabilities across a range of business and technology initiatives. The focus is on establishing scalable and sustainable AI delivery capability while delivering practical business outcomes.

The ideal candidate will have demonstrated experience in AI/ML technologies, building, deploying, monitoring, and evaluating production capabilities as part of a multi-disciplinary team. You will play an important role in developing machine learning models, proofs-of-concept, and AI solutions alongside other machine learning and software engineers, taking solutions from experimentation through to operational use.

The ideal candidate will have proven experience with Agile delivery practices, experience delivering solutions using commercial cloud platforms (AWS, Azure, GCP), experience with DevOps and MLOps practices. They will possess experience with MLOps and DevOps practices, the ability to work autonomously under broad direction, strong communication skills, and a proven ability to collaborate effectively within a team.

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

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. The successful candidate will work within a multidisciplinary team to progress AI initiatives from Proofs of Concepts through to production and operational use. 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.

Work Arrangements

The client is based in Canberra. It is preferred that the successful candidate work from the office with flexible arrangements, however remote working (offsite) will be considered. If based offsite, it is expected that the successful candidate will travel to the client office for 2-3 days to complete onboarding. As well, they are expected to travel to Canberra at least 4 times per year at their own expense.