Data & AI Architect
TypeFull-time job
LocationPhilippines
Posted2 hours ago
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
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
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