Senior AI Architect (For Pooling)

UnionBank of the Philippines; Pasig; On-site; PHP 1,200,000 - 1,800,000; Full time

Job summary

UnionBank of the Philippines is seeking a Senior AI Architect who will define and evolve the enterprise AI architecture. This role involves technical leadership in AI, collaborating closely with cross-functional teams and ensuring compliance with regulatory and security standards.

The ideal candidate should have strong ML/LLM knowledge and substantial experience in AI architecture. Responsibilities include leading solution design and ensuring governance in a regulated environment.

Qualifications

• 7+ years in architecture roles; 3+ years in senior or lead roles.
• Experience in regulated industries preferred.
• Strong leadership and strategic thinking abilities.

Responsibilities

• Define and evolve the enterprise AI architecture blueprint.
• Lead end-to-end AI/ML solution architecture.
• Establish architectural standards for responsible AI.

Skills

ML/LLM knowledge

MLOps

Leadership

Cloud platforms

Analytical abilities

Education

Bachelor’s degree in a relevant field

Cloud/AI certifications

Tools

Snowflake

AWS SageMaker

Job description

Senior AI Architect

Location: UnionBank Plaza, Ortigas, Pasig City

Employment Type: Full-time

About the Role

The Senior AI Architect is responsible for defining, maintaining, and evolving the enterprise AI/ML/Generative AI architecture blueprint, ensuring AI solutions are scalable, secure, governed, and aligned with enterprise strategy. The role provides technical leadership across AI architecture, collaborates closely with business and technology stakeholders, and leads the adoption of modern AI and GenAI capabilities while ensuring compliance with regulatory, security, and responsible AI requirements.

What You’ll Do

AI Architecture Leadership

• Define, maintain, and evolve the enterprise AI/ML/Generative AI architecture blueprint, including solution patterns spanning model pipelines, MLOps/LLMOps frameworks, data flows, and integrations with strategic platforms, data products, and enterprise systems.
• Provide technical leadership and recommendations on the selection, evaluation, and standardization of AI/ML/GenAI technologies, frameworks, and platforms (cloud and on-prem), ensuring alignment with enterprise architecture direction, security requirements, and delivery feasibility.
• Lead architectural design reviews and governance forums (e.g., Architecture Review Boards and technical councils) to ensure AI solutions conform to enterprise architecture principles, security standards, data governance requirements, and responsible AI controls appropriate for a regulated environment.
• Establish and embed AI solutioning into enterprise delivery and AICoE/DSAG processes, ensuring timely data and AI architectural support from early design through execution while accelerating delivery in alignment with governance, security, and regulatory expectations.

Solution Design & Delivery

• Lead end-to-end AI/ML/GenAI solution architecture across the full model lifecycle, from data sourcing to deployment and monitoring, ensuring scalable, secure, and reusable designs aligned with enterprise standards.
• Guide teams on reusable AI architecture patterns and components (e.g., feature stores, vector databases, LLM/agent integration, and model serving) to drive consistency and speed of delivery.
• Provide technical oversight to resolve complex issues across performance, data dependencies, data quality, and model lifecycle management.
• Be accountable for the AI architecture team’s delivery, ensuring outputs are delivered on scope, on budget, at required quality, and within agreed SLAs.
• Contribute to data architecture solutioning as part of the ongoing convergence of data and AI architecture capabilities.
• Continuously evaluate emerging AI (with data) platforms and technologies to enable adoption and facilitate modernization plans. This includes pilot activities that advance AI and Data Maturity.

Governance, Risk, & Compliance

• Establish and maintain architectural standards and guardrails for responsible AI across the model lifecycle, including validation, documentation, and explainability.
• Partner with Model Risk, Cybersecurity, and Data Governance teams to ensure AI solutions comply with regulatory requirements, internal controls, and enterprise risk frameworks.
• Ensure adherence to enterprise AI/ML governance frameworks and best practices through architecture reviews, design guidance, and ongoing oversight.

Stakeholder & Cross-Functional Collaboration

• Partner with business units to translate AI and GenAI strategy into pragmatic technical architectures and roadmaps, in collaboration with data architecture during the group’s transformation.
• Work closely with Engineering, Cloud, Security, and Data teams to ensure seamless integration of AI solutions into enterprise platforms.
• Participate in Architecture Review Boards and technical councils as a subject matter expert in AI & ML architecture, contributing to cross-domain architectural decisions.
• Leadership & Team Development
• Lead and mentor a team of AI and data architects, providing technical guidance and career development.
• Foster a culture of innovation, knowledge sharing, experimentation, and continuous improvement across AI and data architecture domains.
• Support resource planning, capability development, and recruitment for the AI architecture function.

What We’re Looking For

• Bachelor’s degree in a relevant field; cloud/AI certifications are a plus.
• 7+ years in architecture roles; 3+ years in senior or lead roles; experience in regulated industries preferred.
• Strong ML/LLM knowledge, MLOps, cloud platforms, containerization, and responsible AI practices.
• Strong leadership, communication, strategic thinking, and analytical abilities.
• Experience with enterprise data and AI platforms (e.g., Snowflake, AWS SageMaker), agentic AI architectures and frameworks, and the operationalization of Generative AI solutions with strong governance and responsible AI controls in regulated environments is preferred.
• Delivery of scalable and secure AI architecture frameworks.
• Increased reuse and standardization of AI components.
• Compliance alignment and strong governance.
• High stakeholder satisfaction.
• Team capability growth.