Job Title: Cloud Solution Architect - (AI-ML/GCP)
Employment: Full-Time (Onsite)
Required Skill: Google Cloud Platforms (GCP), AI/ML tools, Python , Vertex AI , GCP Certified
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. You'll 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.
Required Skills & Qualifications:
- Bachelor's or master's degree in computer science, Engineering, or related field.
- 5+ 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.
- GCP Professional Cloud Architect or Professional Machine Learning Engineer certification.
Preferred Qualifications:
- 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.
