Technology Architect

Project Role :Technology Architect


Project Role Description :Design and deliver technology architecture for a platform, product, or engagement. Define solutions to meet performance, capability, and scalability needs.
Must have skills :Google Cloud Platform Architecture

Good to have skills :Google BigQuery
Minimum 7.5 year(s) of experience is required

Educational Qualification :15 years full time education

Summary:As a Data Engineer - AI/ML, you will be responsible for designing, building, and maintaining scalable data pipelines and systems that power AI/ML applications on Cloud platforms. Your typical day will involve leveraging Google Clouds data services, implementing GenAI and AI/ML models, and supporting data-driven solutions through efficient architecture and engineering.________________________________________
Roles Responsibilities:i.Design and develop scalable data pipelines and ETL processes using Google Cloud data services like BigQuery, Dataflow, Pub/Sub, and Dataproc.ii.Build and optimize data architectures to support AI/ML applications and model training at scale.iii.Collaborate with data scientists and ML engineers to implement data ingestion, feature engineering, and model-serving pipelines.iv.Develop and manage data integration solutions that align with enterprise data governance and security standards.v.Support GenAI/Vertex AI model deployment by ensuring reliable data access and transformation pipelines.vi.Implement monitoring, logging, and alerting for data workflows and ensure data quality across all stages.vii.Enable self-service analytics by building reusable data assets and data marts for business stakeholders.viii.Ensure cloud-native, production-grade data pipelines and participate in performance tuning and cost optimization.ix.Experience with programming languages such as Python, SQL, and optionally Java or Scala.________________________________________Professional Technical Skills:
Must To Have Skills:
Strong experience in Google Cloud Data Services (BigQuery, Dataflow, Pub/Sub) and hands-on with scalable data engineering pipelines.Good To Have Skills:GenAI/Vertex AI exposure, Cloud Data Architecture, PCA/PDE certifications.Understanding of data modeling, data warehousing, and distributed computing frameworks.Experience with AI/ML data pipelines, MLOps practices, and model deployment workflows.Familiarity with CI/CD and infrastructure-as-code tools (Terraform, Cloud Build, etc.) for data projects.________________________________________
Additional Information:The candidate should have a minimum of 9 years of experience in Google Cloud Data Engineering or related domains.The ideal candidate will possess a strong educational background in computer science, data engineering, or a related field, along with a proven track record of building and scaling data systems for AI/ML initiatives.
Qualification15 years full time education