AI Native Product Architect

Salary: ₱90,000 – ₱130,000 per month

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.