GCP AI Platform Architect / Lead AI Platform Engineer

Primotly is a software house based in Warsaw working on international projects with customers worldwide. They focus on partnership relations, high autonomy, and employee development through courses and mentoring.

• Deep proficiency in designing, deploying, and owning scalable cloud architectures on Google Cloud Platform (GCP), including serverless and containerized microservices.
• Strong programming skills primarily in Python or similar backend languages.
• Hands-on experience building AI agent workflows, RAG (Retrieval-Augmented Generation) pipelines, ReAct prompting, and Vertex AI.
• Proven ability to create secure, high-performance APIs capable of asynchronous communication between decoupled services.
• Practical knowledge of data isolation in multi-tenant SaaS architectures, IAM, and securing cloud-based environments.

• Lead the architecture and hands-on development of a GCP-native, multi-tenant AI SaaS platform.
• Design, build, and optimize scalable backend services and distributed systems using Python and cloud-native technologies.
• Develop and maintain multi-agent orchestration frameworks, including RAG pipelines, ReAct prompting flows, and Function Calling integrations.
• Build secure, high-performance APIs and integrations with external enterprise systems and third-party services.
• Design and implement event-driven and asynchronous communication patterns across decoupled microservices.
• Collaborate on the implementation of AI-native platform capabilities using Vertex AI and modern LLM technologies.
• Ensure platform scalability, reliability, observability, and operational excellence within cloud-based environments.
• Implement security best practices for enterprise SaaS platforms, including IAM, tenant isolation, and secure data access patterns.
• Drive technical direction and architecture decisions while actively contributing to core application development.
• Partner with cross-functional engineering and business teams to translate complex business requirements into scalable technical solutions.
• Improve engineering quality through architecture reviews, code reviews, mentoring, and engineering best practices.
• Support the evolution of platform infrastructure, CI/CD processes, and containerized deployment workflows.