This role is based in Sydney, Australia

We’re partnering with a leading financial-services firm that is expanding its AI capability across APAC and investing heavily in next-generation conversational and agentic AI systems.

This is a rare opportunity to step into a senior role where you’ll shape the firm’s AI adoption, build Python-based LLM solutions used daily by traders and engineers, and lead the integration of RAG pipelines, vector databases, and agentic AI workflows across global teams.

If you want to build real-world AI systems in an environment where the business actually adopts what you create — this is one of the most impactful roles in the region.

What You’ll Work On

• Build and optimise LLM-powered conversational systems supporting front-office and technology teams
• Develop Python-based agentic workflows, automation layers, and knowledge-retrieval systems
• Architect APIs, data integrations, and microservices connecting AI tools with enterprise applications
• Implement and scale RAG pipelines, vector database integrations, and retrieval-optimised search
• Prototype and evaluate emerging AI/ML technologies for practical production use
• Lead engineering best practices and mentor technical contributors across APAC
• Partner with global engineering, trading, and research teams on strategic AI programmes

What You Bring

• 10+ years of enterprise engineering experience (Python preferred or C#)
• Strong experience with LLMs, NLP, conversational AI, or OpenAI/Azure AI frameworks
• Hands-on experience with RAG, vector DBs (e.g., Pinecone, Milvus, Chroma), embeddings
• Strong foundations in REST APIs, microservices, integrations, and cloud platforms
• Exposure to Docker, Kubernetes, CI/CD, and DevOps tooling
• Ability to drive technical direction and collaborate across global regions
• A curiosity-driven mindset, interest in experimentation, and clear communication style

You’ll be joining a team that values strong engineering fundamentals, modern AI/ML practices, and building tools that meaningfully improve how people work.