[Hiring] Data & AI Architect @Dysrupit

Role Description

The architect will lead both pre-contract technical solutioning and subsequent delivery, preserving continuity from proposal to production. Joining a small, senior practice means early client exposure, fast architectural decisions, hands-on implementation and direct influence over delivery standards and reusable assets. It also means varied, ambiguous work and occasionally creating the playbook; candidates seeking a tightly defined remit may not find the role suitable. Time is split approximately equally between client engagement and delivery, flexing with the pipeline. Typical work includes:

• Discovery workshops
• Target-state architecture
• Proposal estimates
• Code review
• Building a retrieval-augmented vertical slice
• Client enablement
• Steering-committee presentations
• Converting lessons into reusable patterns

Qualifications

• About eight years in data/AI engineering and architecture, including three years with substantive design authority and senior client-facing consulting exposure.
• Databricks: lakehouse architecture, Delta Lake, Unity Catalog, Spark/PySpark, Lakeflow or Delta Live Tables, orchestration, performance and cost optimisation.
• Azure/Microsoft: Data Factory or Fabric, ADLS, Azure OpenAI or AI Foundry, Entra ID and networking.
• Generative AI: production RAG, vector stores, agents/tool use, evaluation, guardrails and prompt/context engineering, including Claude or an equivalent frontier model.
• Strong production Python and SQL; sound data-modelling judgement across dimensional, data vault and wide denormalised approaches.
• DevOps/MLOps fundamentals: version control, CI/CD, infrastructure as code, containers and monitoring.
• Excellent written and spoken English for executive proposals, decision records and presentations.

Requirements

• Databricks Professional or Azure Solutions Architect Expert certification.
• Big Four, global systems integrator or specialist consultancy experience, including bids, statements of work and estimation.
• Applied responsible AI governance and delivery experience in financial services, retail or travel.
• Experience with Australian/APAC clients, Snowflake, dbt, Power BI or Fabric, and mentoring small engineering teams.