Data & AI Architect

DysrupIT; Manila; On-site; PHP 2,400,000 - 4,800,000; Full time

Job summary

DysrupIT seeks a senior Data/AI Architect to lead both pre-contract technical solutioning and on‑going delivery. The role blends client engagement with hands‑on implementation, delivering end‑to‑end architecture for Databricks lakehouses, Delta Lake and production AI systems.

You will influence delivery standards, build reusable patterns, and mentor a small delivery team. The ideal candidate has eight years in data/AI, strong Python/SQL, and experience with large-scale AI platforms, cloud data

Qualifications

• Approximately eight years in data/AI engineering and architecture with senior client-facing consulting exposure.
• Databricks lakehouse architecture, Delta Lake, Unity Catalog, Spark/PySpark, and orchestration experience.
• Strong production Python and SQL; sound data modelling across dimensional, data vault and wide denormalised approaches.
• Excellent written and spoken English for executive proposals, decision records and presentations.

Responsibilities

• Lead pre-sales solutioning, shape approaches and facilitate discovery workshops.
• Own end-to-end architecture; lead delivery, scope, stand-ups and client technical discussions.
• Deliver Databricks lakehouses and associated components; build production AI systems including RAG, agents, evaluation and orchestration.
• Set CI/CD, infrastructure as code, testing, observability and cost standards; mentor client engineers and ensure production readiness.
• Turn delivery experience into reusable patterns, accelerators and estimation models.

Skills

Data/AI engineering

Solution architecture

Python

SQL

Databricks

CI/CD

MLOps

English communication

Client-facing consulting

Tools

Delta Lake

Unity Catalog

Spark/PySpark

Delta Live Tables

dbt

Job description

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.