AI Solutions & Delivery Specialist

We’re Jetstar. You may know us for our famous low fares, but did you also know that we’ve flown more than 400 million customers since we launched in Australia in 2004? In fact, each week we operate around 3,000 flights to nearly 40 destinations across Australia, New Zealand, Asia and the Pacific. We also operate a fleet of more than 80 aircraft, including state-of-the-art Airbus A320/21 NEO aircraft and the Airbus A320 family and Boeing 787-8s. Every day we enable more people to takeoff to more places, more often.

Impressive numbers? What's even more impressive is the team of extraordinary people taking off together day in day out, flight after flight.

Our people are the real jet stars!

• Fantastic opportunity to influence AI Solutions within Data Services, Emerging Technologies
• Support Jetstar’s overall tech strategy, driven by business and technology requirements
• Full-time permanent role based in Melbourne

The AI Solutions & Delivery Specialist is responsible for leading AI experimentation and prototype delivery within the Jetstar Rapid Validation Lab, under the Emerging Technologies team. The role drives the structured lifecycle from use-case discovery through PoC and working prototype, producing deployment-ready blueprints that business units and technology partners take forward to production.

Within the AI emerging technologies stream, this role does not operate production systems, its value is in de-risking and accelerating what gets built, and in the quality of the artefacts handed off.

Within the data science and ML engineering stream, the role owns the full classical ML lifecycle from problem framing and feature engineering through production deployment and monitoring

Key responsibilities of the role include:

• Influence the technical delivery of AI, Agentic AI and GenAI proof-of-concepts from discovery through to working prototype.
• Define PoC scope, hypotheses, success measures and exit criteria, producing evidence-based proceed, pivot or stop recommendations.
• Create deployment-ready artefacts including solution architecture documentation, agent blueprints, autonomy boundaries and runbooks.
• Build and maintain a reusable internal AI agent catalogue, ensuring each PoC produces a versioned enterprise blueprint.
• Apply data science expertise to PoC design and evaluation, including model selection, bias assessment and performance baselines.
• Assess AI platforms and engage with technology partners during PoC build phases as the technical lead.
• Support executive-facing materials and technical education sessions with accurate solution narratives and AI subject matter expertise.
• Contribute to AI governance, cybersecurity, privacy and regulatory compliance, championing responsible AI practices across experimentation.
• Maintain the weighting of the role as data science and ML engineering allocation by designing, deploying and operating classical ML models, MLOps pipelines and production data features.

The Ideal candidate will bring:

• Proven experience delivering AI or data-driven solutions from discovery through to validated prototype in an enterprise environment.
• Strong knowledge of AI/ML concepts, agentic AI architectures, large language models and cloud-based AI services across AWS, Azure or GCP.
• Ability to assess AI platforms and tools against PoC requirements and document clear technical findings for evaluation.
• Skilled in producing solution architecture artefacts, deployment process maps and handoff-ready technical documentation.
• Familiarity with AI governance, responsible AI principles, and enterprise security and compliance frameworks.
• Ability to present technical findings to both technical and non-technical stakeholders.
• Delivery-focused, with the ability to manage competing priorities, navigate ambiguity and drive outcomes without direct authority.
• Data science proficiency across model fitness assessment, RAG pipeline design, bias and fairness assessment, and statistical analysis.
• Hands-on experience with classical machine learning, feature engineering, model validation, MLOps practices and production model operations.
• Technical capability preferred; Python, Snowflake, Snowpark, SQL, and standard ML/statistics libraries such as scikit-learn, pandas, statsmodels, XGBoost or LightGBM.

Your life at Jetstar is unlike any other

Live the travel dream: You and your nominated travel companions can take-off more with our famous Qantas Group Staff Travel discount.

Make work feel like a holiday: Join a fun, driven team where work feels like a holiday. Our people take every opportunity to take-off together and find ways to help their teammates soar.

There’s a place for everyone at Jetstar: For us, travel and meeting new people go hand in hand. We invite our people to get involved in various Employee Network Groups that play an important role in creating our inclusive and supportive culture that we are fiercely proud of. We believe that teams with a diversity of ideas and experiences are more creative, innovative and solve problems quickly. We welcome applications from anyone who wants to take-off with us. If you have support or access requirements, just reach out and let us know what you need.

We’re Jetstar. Let’s take-off together!

Applications will close by Friday the 11th of September