Data & AI Architect

JOB SUMMARY

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 and converting lessons into reusable patterns.

JOB RESPONSIBILITIES:

Solutioning and pre-sales

• Shape technical approaches, challenge problem statements and facilitate discovery workshops.
• Produce target-state architectures, build sequences, proposal assumptions, exclusions, risks and defensible estimates.
• Design four-to-six-week proofs of concept and serve as technical peer to client architects, data leaders and CIOs.

Delivery and hands-on architecture

• Own end-to-end architecture and, where required, lead delivery, scope, stand-ups and client technical
• Remain hands-on, implementing demanding components and reference solutions.
• Deliver Databricks lakehouses, including medallion layers, Unity Catalog, Delta Lake, ingestion and orchestration.
• Build production generative AI systems covering RAG, agents, evaluation, prompt/context engineering, cost and latency.
• Set CI/CD, infrastructure-as-code, testing, observability and cost standards; mentor client engineers and manage production readiness and handover.

Practice capability and intellectual property

• Turn delivery experience into reference architectures, accelerators, templates and estimation models.
• Contribute to Frontier Academy and maintain current recommendations across Databricks, Microsoft and Anthropic.
• Help shape and eventually lead a small delivery team, including recruitment.

JOB QUALIFICATIONS:

Must Have:

• 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.

Desirable:

• 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.

Originally posted on Himalayas