Solution Architect

6 hours ago

Pune, India Ishan Technologies Full time

Job Title: Solution Architect - (AI-ML/GCP) Employment: Full-Time (Onsite) Required Skill: Google Cloud Platforms (GCP), AI/ML tools, Python 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. 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.

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