We are looking for an experienced GCP AI Architect to design, implement, and govern enterprise-scale cloud and artificial intelligence solutions on Google Cloud Platform (GCP). The ideal candidate will have strong expertise in GCP services, Vertex AI, Generative AI, cloud-native architecture, and MLOps, with the ability to build scalable, secure, and cost-efficient AI platforms.

Key Responsibilities :

- Design and architect enterprise-scale cloud and AI solutions on Google Cloud Platform.

- Leverage GCP services, including Compute Engine, Google Kubernetes Engine (GKE), Cloud Run, BigQuery, Cloud Storage, Pub/Sub, and Dataflow.

- Architect and implement AI/ML solutions using Vertex AI, Gemini, Generative AI, and Retrieval-Augmented Generation (RAG).

- Design and implement scalable AI platforms, microservices, and cloud-native applications.

- Establish MLOps practices for model deployment, monitoring, automation, and lifecycle management.

- Lead cloud migration, modernization, and enterprise AI transformation initiatives.

- Implement infrastructure automation using Terraform and establish CI/CD and DevSecOps pipelines.

- Define cloud architecture standards, governance frameworks, and best practices.

- Ensure cloud security through effective IAM, networking, access controls, and security policies.

- Optimize cloud infrastructure performance, scalability, reliability, and operational costs.

- Collaborate with engineering, data science, DevOps, and business teams to deliver enterprise AI solutions.

- Drive FinOps practices and cloud cost optimization across cloud and AI workloads.

Required Skills :

- 10+ years of experience in cloud architecture, solution architecture, or related technical roles.

- Strong hands-on expertise in Google Cloud Platform (GCP) services and architecture.

- Experience with Compute Engine, GKE, Cloud Run, BigQuery, Cloud Storage, Pub/Sub, and Dataflow.

- Strong knowledge of Vertex AI, Gemini, Generative AI, and RAG architectures.

- Experience designing and implementing MLOps pipelines and AI/ML platforms.

- Expertise in cloud-native architecture, microservices, Kubernetes, and distributed systems.

- Proficiency in Terraform, CI/CD automation, and DevSecOps practices.

- Strong understanding of cloud networking, IAM, security, governance, and compliance.

- Experience with cloud migration, modernization, scalability, and performance optimization.

Preferred Qualifications :

- Programming experience with Python and/or Java.

- Experience with API development and integration.

- Knowledge of FinOps and cloud cost management practices.

- Exposure to multi-cloud environments.

- Google Professional Cloud Architect certification is preferred.