Google Cloud Professional Cloud Architect

Define and evolve AI & Data architecture strategy and roadmap, aligned with business priorities and IT strategy. Serve as a thought leader for modern data, analytics, and AI architectures, including Generative AI and Agentic AI. Identify, evaluate, and recommend emerging technologies, platforms, and architectural patterns. Partner with business and digital leaders to identify and prioritize high-impact AI and analytics use cases. Provide architectural guidance on ethical, responsible, and compliant AI adoption.

Solution Architecture & Platform Design Lead end-to-end architecture design for complex data, analytics, and AI initiatives, ensuring scalability, performance, security, and cost efficiency. Design and govern cloud-based data platforms leveraging: Google Cloud Platform (BigQuery, Vertex AI, Dataflow, Dataproc, Looker) AWS (S3, Glue, EMR, Redshift, SageMaker, Lambda) Snowflake (data warehouse, data sharing, performance optimization) Architect modern enterprise data architectures, including: Data Lake, Lakehouse, Data Mesh, and Data Fabric Open table/file formats such as Parquet, Iceberg, Delta Lake Medallion architectures (Bronze/Silver/Gold) Define data ingestion and integration patterns across structured and semi-structured sources (SAP, Oracle, Salesforce, JDE, Ariba, IoT, APIs, NoSQL). Define and enforce data quality, metadata, lineage, and access control standards.

AI, ML, and Generative AI Architecture Design and implement AI/ML and GenAI solution architectures from experimentation through production. Architect solutions for core ML use cases such as demand forecasting, predictive maintenance, supply chain optimization, and customer analytics. Lead architecture for Generative AI and Agentic AI, including: LLM integration with tools, APIs, and knowledge bases (RAG patterns) Autonomous and semi-autonomous agent workflows Fine-tuning, prompt engineering, and optimization strategies Establish MLOps and LLMOps frameworks for model training, deployment, monitoring, evaluation, and lifecycle management. Define approaches for model observability, explainability (XAI), bias detection, and risk mitigation.

Technical Leadership & Collaboration Provide technical leadership and mentorship to solution architects, data engineers, data scientists, and AI engineers. Collaborate closely with platform, DevOps, and cloud engineering teams to enable automation-driven deployments. Review solution designs, conduct architecture assessments, and provide impact analysis and recommendations. Communicate complex technical concepts clearly to both technical and executive audiences.

Bachelor's Degree in Engineering or a related technical discipline. 14+ years of hands-on experience in data architecture, analytics solutions, and/or cloud data platforms. 3+ years of hands-on experience delivering AI/ML and Generative AI solutions in production. 6+ years of experience designing and scaling enterprise data platforms on Google Cloud Platform, AWS, and Snowflake., Master's degree or Ph.D. preferred. Demonstrated success leading large-scale, cross-functional data and AI initiatives. Cloud platforms: Google Cloud Platform and AWS (multi-cloud experience strongly preferred) Data platforms: Snowflake, BigQuery, Data Lakes, Lakehouse architectures Programming & analytics: Python, SQL, PySpark AI/ML frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost GenAI/LLM frameworks, vector databases, and graph databases Data engineering tools: Spark, Kafka, Hadoop Containerization and orchestration: Docker, Kubernetes

CI/CD and DevOps practices Strong understanding of data modeling, performance tuning, and cost optimization Strong architectural thinking and problem-solving skills Excellent communication and stakeholder management capabilities Ability to influence without authority and operate effectively in matrixed organizations Self-driven, organized, and able to manage multiple priorities