Technology
AI Lead
Our client is seeking a hands-on AI Lead to design, build and ship AI systems that deliver real, measurable business value. This is a builder's role, not a strategist's — you'll personally architect the solutions, write the code, wire up the data and stand systems up in production. You'll own AI delivery end to end, from framing the problem through to deployment, monitoring and iteration, keeping every initiative commercially grounded and well governed. If you'd rather be in the code than in a strategy deck, this one's for you. This is a fully office-based role in Hawthorn — five days on-site with the team.
What you'll be doing
- Personally architect and build AI/ML and generative AI solutions — from rapid prototypes and proofs of concept through to production systems.
- Write, test and maintain production-quality code, and own the technical calls on model selection, architecture, data pipelines and tooling.
- Own the full lifecycle of each AI system — problem framing, data prep, model development, evaluation, deployment and ongoing iteration.
- Design and implement generative AI applications including RAG pipelines, agentic workflows and LLM-based automation.
- Build rigorous evaluation frameworks that measure accuracy, reliability, latency, cost and safety.
- Design the data architectures and pipelines that make enterprise data usable for AI — ingestion, transformation, embeddings and vector stores.
- Integrate AI systems with existing platforms, APIs and business processes, including our client's Microsoft Dynamics ERP.
- Establish MLOps / LLMOps practices — version control, CI/CD, model registries,monitoring, observability and cost controls — while keeping systems secure, scalable and resilient.
- Shape a pragmatic AI roadmap: prioritise high-impact use cases, tie every initiative to a measurable outcome, and make (and defend) build-vs-buy decisions.
- Set practical AI governance and guardrails, and advise executives on capabilities, timelines and risk while lifting AI literacy across the business.
What you'll bring
- 7+ years designing, building and deploying software and ML/AI systems — with recent, hands-on delivery of production AI. You're still in the code, not just directing others.
- A track record shipping generative AI applications (LLMs, RAG, agents) and/or ML models that are live in production.
- Hands-on experience designing and building AI agents / agentic systems — tool-using, multi-step autonomous workflows — and taking them to production.
- Strong programming ability, particularly Python, in a modern engineering workflow (Git, testing, CI/CD, cloud).
- Deep, practical knowledge of the modern GenAI stack — foundation-model APIs, RAG, vector databases, orchestration/agent frameworks, prompt engineering and fine-tuning.
- Solid grounding in ML frameworks (e.g. PyTorch, scikit-learn) and cloud AI platforms (AWS Bedrock/SageMaker, Azure AI/OpenAI or GCP Vertex AI), plus containerisation and deployment.
- Data engineering fundamentals — SQL, pipelines, and working with structured and unstructured data at scale. Experience integrating with enterprise systems (our client runs Microsoft Dynamics) is an advantage.
- Commercial judgement and the communication skills to move between deep technical detail and an executive conversation.
- A degree in Computer Science, Software/Data Engineering, ML or a related quantitative discipline — or equivalent practical experience.
- A pragmatic, outcome-driven mindset — biased toward shipping working systems, with a strong sense of responsibility around risk, security and ethics.
