Applied AI Engineer, Australia

Applied AI Engineer, Australia at Mistral AI scores 91 out of 100 on AI centrality, which makes it a Level 4 role on this board.

SydneyFullTime

AI in this role

ragfine-tuningai-research

About Mistral

Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms.

We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.

The Role

As a Applied AI Engineer on the Applied AI team, you will bridge the gap between Mistral’s cutting-edge AI research and real-world enterprise applications. This role exists to ensure our solutions are robust, scalable, and aligned with both customer needs and Mistral’s technological vision.

The Applied AI team is Mistral’s customer-facing technical organization, working directly with enterprise clients from pre-sales through implementation. We combine deep machine learning expertise with strong customer engagement, operating like startup CTOs who own end-to-end project execution.

By joining this team, you will drive the adoption of Mistral’s products, helping customers deploy AI solutions that deliver measurable business impact across industries.

What You Will Do

Deploy production-ready AI use cases with significant business impact across diverse industries.
Develop state-of-the-art GenAI applications, from consumer products to industrial solutions, driving technological transformation for customers.
Collaborate with researchers, AI engineers, and product teams on complex customer projects involving fine-tuning, advanced LLM applications, and contributions to open-source codebases.
Participate in pre-sales calls to understand client needs, challenges, and aspirations, providing technical guidance on Mistral’s products.
Work with product and science teams to continuously improve model and product capabilities based on customer feedback.

What We're Looking For

Fluency in English.
Experience as a technical individual contributor (data scientist or software engineer) on AI-based products.
Proven track record in implementing AI or machine learning products with APIs, back-end, and front-end interfaces.
Hands-on experience with fine-tuning LLMs, advanced RAG, or agentic use cases.
Deep understanding of machine learning and LLM concepts and algorithms.
Strong technical coding skills in Python.
Ability to explain complex technical concepts clearly to both technical and non-technical audiences.
Contributions to open-source projects, particularly in the LLM space.

What We Offer

We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.

For the most up-to-date details on benefits available in your location, please refer to our Benefits page.

Privacy Policy

Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RagFine TuningAI Research

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
Tell me about a research question you investigated. What did you find?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Rag, Fine Tuning, and AI Research. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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