Sydney | Hybrid Working
Profusion Group is proud to be partnering with a top bank to hire a Lead AI Engineer — supporting one of the bank’s most strategic transformation agendas.
This is a rare opportunity to shape how AI and Generative AI are embedded at enterprise scale, driving innovation across core data platforms while ensuring safety, governance, and real-world impact.
The Opportunity as a Lead AI Engineer, you will play a pivotal role in uplifting AI engineering capability and leading the delivery of production-grade GenAI solutions.
You’ll operate at the intersection of engineering, data, and platform, setting technical direction and building scalable AI systems that integrate into a modern cloud ecosystem.
This is not a research role—you’ll be hands-on designing, building, and deploying real, enterprise-grade AI applications that are secure, resilient, and cost-efficient.
- Work on enterprise-scale AI transformation within one of Australia’s most recognised financial institutions
- Build solutions that move beyond pilots into real production impact
- Influence how AI is safely adopted across a major organisation
- Collaborate with top-tier engineers, architects, and data professionals
- Flexible and hybrid working arrangements
- Career-defining opportunity in a high-growth AI capability area
- Access to employee benefits including banking discounts and wellbeing initiatives
- Ongoing learning and development in a cutting-edge technical domain
- Lead the end-to-end delivery of AI and GenAI use cases from discovery through to deployment and production support
- Design and build agentic and GenAI solutions using modern patterns such as RAG, tool use, and guardrails
- Define and evolve reference architectures, standards, and reusable frameworks across the AI engineering landscape
- Integrate AI into cloud-native data platforms, ensuring scalability, performance, and security
- Embed LLMOps/MLOps practices including CI/CD, evaluation, monitoring, and operational governance
- Partner with product, engineering, and risk teams to operationalise AI safely within the SDLC
- Drive capability uplift through mentoring, coaching, and engineering best practice
- Strong hands-on expertise with GenAI frameworks such as LangChain, AutoGen, DSPy or similar
- Proven track record delivering production GenAI solutions, including RAG, agent workflows, prompt engineering, and evaluation
- Demonstrated technical leadership across cross-functional teams
- Solid engineering background with Python and experience building scalable services and APIs
- Experience embedding AI into the software development lifecycle (CI/CD, testing, monitoring)
- Strong experience working in cloud environments (AWS, Azure, or GCP)
- Familiarity with containerisation (Docker, Kubernetes)
- Financial services or regulated environment experience
- Exposure to AI governance, model risk, and security frameworks
- Experience in real-time/streaming AI systems
