At Synechron, we believe in the power of digital to transform businesses for the better. As a global consulting firm, we combine creativity and innovative technology to deliver industry-leading digital solutions. Our expertise spans Data, AI, Cloud, Digital, and Infrastructure — helping organisations modernise, scale, and innovate.

We’re looking for a Graduate AI & Data Consultant to join our team in January 2027.

This is an early-career role for someone who is curious about AI and technology, enjoys solving problems and wants to learn by building real solutions. We’re not expecting you to arrive knowing every platform or tool. Many of the skills you’ll need will be developed on the job.

You’ll work alongside experienced consultants on real client problems, with opportunities spanning AI, data and software development.

What we’re looking for

We’re interested in both your technical foundations and how you think, learn and solve problems. You don’t need years of experience, but ideally you’ll have some exposure to:

- Python fundamentals: Comfortable writing clean, readable code, working with pandas, calling APIs, handling JSON and basic debugging.
- LLM fundamentals: An understanding of concepts such as tokens, context windows, temperature, hallucination and why prompting matters.
- Prompt engineering: Able to write structured prompts and iterate on them deliberately.
- RAG basics: An understanding of chunking, embeddings, vector search, retrieval and generation, and the importance of retrieval quality.
- SQL: Able to perform basic queries, joins and aggregations.
- Problem solving: Able to take a problem, break it down and work towards a practical solution.




- Communication: Able to explain technical concepts and your own work clearly, including to non-technical audiences.
- Learning agility: A willingness to experiment, learn quickly, ask good questions and pick up unfamiliar technologies.
- AI-assisted development: An interest in using AI tools to accelerate development and improve how you solve problems.

Nice to have

You don’t need experience across all of these, but exposure to any of the following would be a bonus:

- Agentic coding tools: Claude Code, Cursor or GitHub Copilot.
- Agent frameworks and patterns: An understanding of concepts such as orchestrator/subagent patterns, tool calling and human-in-the-loop approaches.
- Model Context Protocol (MCP): Awareness of what it is and how it can be used to connect AI agents with tools and systems.
- AI evaluation: Exposure to approaches such as LLM-as-judge, test sets and measuring retrieval and answer quality.
- Responsible AI and guardrails: Awareness of PII handling, prompt injection, grounding and citations.
- Databricks: Exposure to notebooks, Unity Catalog, Delta tables or Databricks AI capabilities.
- Azure: Exposure to Azure OpenAI, Azure AI Foundry or Azure AI Search.
- Web and API development: Experience with FastAPI, Flask or similar.
- Docker and cloud deployment:



Some familiarity with packaging and deploying applications.

Most importantly, we’re looking for potential rather than a finished skill set. We know graduates may not have had the opportunity to work with all of these technologies yet. If you’ve built something engaging, taught yourself a new technology, experimented with AI or used technology to solve a problem, we’d love to hear about it.

Our recruitment process is designed to understand how you think and solve problems, not just what technologies you already know.

1. Apply

Submit your application and tell us a little about yourself and why this opportunity interests you.

2. Initial interview

Selected applicants will be invited to an initial screening interview where we’ll discuss your interests, experience and approach to problem solving.

3. Project and panel interview

Candidates progressing to the final stage will be asked to present a personal, or work project relevant to the role.

If you don’t already have a suitable project, we’ll provide a small problem brief and give you two weeks to develop a proof of concept. You’ll be encouraged to use AI and other development tools as part of the process.

During the panel interview, we’ll ask you to:

- Explain the problem and intended outcome
- Walk us through your approach and architecture
- Demonstrate your solution
- Discuss the decisions and trade-offs you made
- Walk through selected parts of your code

We’re interested in how you approached the problem, what you learned and how well you can explain your thinking, not whether the solution is perfect.

📌 Graduate AI & Data Consultant (Adelaide)
🏢 Synechron Enterprise
📍 Adelaide