AI Native Product Architect

White Cloak Technologies, Inc.; Metro Manila; On-site; PHP 1,400,000 - 2,000,000; Full time

Job summary

White Cloak Technologies, Inc. is seeking an AI-Native Product Architect to design, validate, and scale software and AI-powered solutions in a business-technical context.

The role blends strategy with engineering execution, delivering secure, scalable architectures and accelerating delivery through AI engineering leadership. You will lead architecture for LLMs, agents, RAG, and workflow automation, define stacks and cloud strategies, prototype MVPs, ensure security and observability, and drive

Qualifications

• 5+ years in software engineering with 3+ years in technical lead or architect roles.
• Experience building scalable cloud-native distributed systems.
• Hands-on deployment on AWS/Azure/GCP including networking, security, and cost optimization.
• Delivery of MVPs, prototypes, and production-ready solutions.
• Hands-on AI engineering with LLMs, agents, workflows, or RAG.
• Knowledge of architectural patterns: Clean Architecture, Microservices, EDA, DDD, CQRS.
• Experience with containers, Kubernetes, CI/CD, DevOps, Terraform, and observability.

Responsibilities

• AI-Native solution architecture: integrate LLMs, agents, RAG, and workflow automation into products.
• Architecture planning: define stacks, integration patterns, cloud strategies, and implementation roadmaps.
• MVP and validation: lead prototypes and proofs-of-concepts to reduce risk and accelerate time-to-market.
• AI harness engineering: build reusable prompts, agents, evaluation frameworks, and engineering accelerators.
• Technical leadership: ensure scalability, security, maintainability, observability, and cost efficiency.
• Architecture governance: establish standards, reference architectures, and reusable assets for consistency.
• Emerging technology research: evaluate and recommend AI, cloud, data, and engineering technologies.

Skills

Cloud architecture

AWS/Azure/GCP

Microservices

Kubernetes

CI/CD

Terraform

Observability

Security

Tools

Cloud platforms

Containers

Job description

AI-Native Product Architect designing, validating, and scaling software and AI-powered solutions, bridging business strategy and engineering execution. The role drives AI-native product development, technical innovation, and digital transformation by delivering secure, scalable, maintainable architectures and accelerating delivery through AI engineering and leadership.

Roles & Responsibilities

1. AI-Native solution architecture: integrate LLMs, agents, RAG, and workflow automation into products.

2. Architecture planning: define stacks, integration patterns, cloud strategies, and implementation roadmaps.

3. MVP and validation: lead prototypes and proof-of-concepts to reduce risk and accelerate time-to-market.

4. AI harness engineering: build reusable prompts, agents, evaluation frameworks, and engineering accelerators.

5. Technical leadership: ensure scalability, security, maintainability, observability, and cost efficiency.

6. Architecture governance: establish standards, reference architectures, and reusable assets for consistency.

7. Emerging technology research: evaluate and recommend AI, cloud, data, and engineering technologies.

Required Qualifications

1. 5+ years software engineering; 3+ years technical lead/architect leadership experience.

2. Scalable cloud-native distributed systems architecture experience.

3. Hands-on AWS/Azure/GCP deployment: networking, security, scalability, and cost optimization.

4. Enterprise MVPs, prototypes, and production-ready solution delivery experience.

5. Hands-on AI engineering: LLMs, AI agents, workflow automation, or RAG architectures.

6. Architectural patterns: Clean Architecture, Microservices, Event-Driven, DDD, and CQRS.

7. Containers, Kubernetes, CI/CD, DevOps, Terraform, and observability practices.

Preferred Qualifications

1. Ability to translate ambiguous requirements into prototypes and production-ready architectures.

2. Experience establishing prompt engineering, agent orchestration, AI testing, and model evaluation standards.

3. Experience driving AI-assisted development workflows using coding assistants and automated testing.