Forward Deployed Engineer, AI Forward Deployed Engineering team. Melbourne, Australia. Open to remote locations. Customer-facing: build and productionize first-of-its-kind GenAI applications alongside Databricks customers.
Embedded with the customer, owning the build end-to-end, presenting back to senior stakeholders, iterating in production. The label is new to my CV; the work isn't. Pacific Brands was FDE before FDE was a recognised role. Saputo's DOT system was FDE inside the customer organisation.
The Databricks Australian customer base — Coles, NAB, Macquarie, Telstra, Westpac NZ, Atlassian — is dominated by enterprises whose data and ML maturity is uneven. They need someone who speaks both supply chain and Python, both finance and prompt engineering. I'm closer to that bridge than most candidates with stronger ML CVs.
Layers 5–7 of the DOT architecture and the KIM pegging engine were built with Claude as engineering partner from 2024 onward. RAG patterns, prompt chains, agentic exception handling, multi-step reconciliation. Not toy projects — enterprise-scale build patterns at the limits of what one person + AI can architect. The methodology is what Databricks customers are asking their FDEs to teach them.
The role description explicitly says you build the AI FDE team as an ensemble of unique specialisations. Mine: enterprise-customer-embedded planning systems delivery, three industries deep, with two years of Claude-as-build-partner methodology layered on. Most strong AI engineers don't have the customer-side enterprise spine. Most strong customer-side enterprise builders don't have the GenAI methodology yet. The overlap is small — and that's where I sit.
Operating at Workwear Group level — three management tiers above where I sit today — built a single planning engine across the entire group. Harvested data from six divisional ERPs, auto-generated supplier orders, produced branded dashboards per division and S&OP decks for the three divisional CEOs with mandatory sign-off governance. Extended into NZ inside the original delivery window.
The relevant point for Databricks: this was Forward Deployed Engineering before the term existed. Embedded inside the customer organisation, owning the build, presenting back to executives, iterating in production.
Built the DOT (Demand & Operations Toolkit) reporting system over nine years, supporting the full Australian operation. Seven integrated layers covering demand sensing, master scheduling, supply planning, inventory deployment, financial reconciliation, and exception management. Layers 5–7 (the most recent) were built with Anthropic's Claude as a genuine engineering partner — not Microsoft Copilot autocomplete, full architectural collaboration.
Used by 600+ planners, schedulers, supply analysts and operations leaders every working day. The methodology is the methodology Databricks customers buy FDE engagements to learn.