Ishan Technologies - Solution Architect - Google Cloud Platform

Job Summary :

Solution Architect with deep proficient knowledge in Google Cloud Platform (GCP) and AI services to lead the design and deployment of intelligent, scalable, and secure cloud solutions.

Youll play a pivotal role in shaping our cloud and AI strategy, bridging business needs with technical execution and driving innovation across the organization.

Key Responsibilities :

- Architect and implement end-to-end solutions using GCP services, with a focus on AI and machine learning capabilities.

- Collaborate with stakeholders to gather requirements and translate them into scalable, intelligent architectures.

- Lead cloud migration and modernization initiatives, integrating AI-driven components where applicable.

- Design and deploy models using GCP AI tools such as Vertex AI, BigQuery & Custom model development.

- Define best practices for cloud security, scalability, and cost optimization.

- Create architectural diagrams, documentation, and technical roadmaps.

- Provide technical leadership and mentorship to development and data science teams.

- Conduct architecture reviews and ensure alignment with enterprise standards.

- Stay current with GCP updates, AI trends, and emerging technologies.

Required Skills & Qualifications :

- Bachelors or masters degree in computer science, Engineering, or related field.

- 7+ years of experience in software development and architecture roles.

- 3+ years of hands-on experience with GCP services (Compute Engine, Cloud Functions, BigQuery, Cloud Storage, etc.)

- Proven experience with GCP AI/ML services (Vertex AI, AutoML, BigQuery ML, etc.)

- Strong understanding of cloud-native design, microservices, and container orchestration (Kubernetes, GKE).

- Experience with CI/CD pipelines and DevOps practices.

- Excellent communication and stakeholder engagement skills.

- Ability to balance technical depth with business strategy.

Preferred Qualifications :

- GCP Professional Cloud Architect or Professional Machine Learning Engineer certification.

- Experience with hybrid and multi-cloud environments.

- Familiarity with infrastructure-as-code tools (Terraform, Deployment Manager).

- Background in data architecture, model lifecycle management, and MLOps.