Cloud Architect

Cloud Architect

Location : San Jose, CA need local

Job Description :

Strategy, Planning, and Roadmap Development : Align AI and ML system design with broader business objectives, shaping technology roadmaps and architectural standards for end-to-end cloud-driven analytics and AI adoption.

Designing End-to-End AI / ML Workflows : Architect and oversee all stages of AI / ML pipeline development-data ingestion, preprocessing, model training, validation, deployment, monitoring, and lifecycle management within cloud environments.

Selecting Technologies and Services : Evaluate and choose optimal cloud services, AI / ML platforms, infrastructure components (compute, storage, orchestration), frameworks, and tools that fit operational, financial, and security requirements.

Infrastructure Scalability and Optimization : Design and scale distributed cloud solutions capable of supporting real-time and batch processing workloads for AI / ML, leveraging technologies like Kubernetes, managed ML platforms, and hybrid / multi-cloud strategies for optimal performance.

MLOps, Automation, and CI / CD Integration : Implement automated build, test, and deployment pipelines for machine learning models, facilitating continuous delivery, rapid prototyping, and agile transformation for data and AI-driven products.

Security, Compliance, and Governance : Establish robust protocols for data access, privacy, encryption, and regulatory compliance (e.g., GDPR, ethical AI), coordinating with security experts to continuously assess risks and enforce governance.

Business and Technical Collaboration : Serve as the liaison between business stakeholders, development teams, and data scientists, translating company needs into technical solutions, and driving alignment and innovation across departments.

Performance Evaluation & System Monitoring : Monitor infrastructure and AI workloads, optimize resource allocation, troubleshoot bottlenecks, and fine-tune models and platforms for reliability and cost-efficiency at scale.

Documentation and Best Practices : Create and maintain architectural diagrams, policy documentation, and knowledge bases for AI / ML and cloud infrastructure, fostering a culture of transparency, learning, and continuous improvement.

Continuous Innovation : Stay abreast of new technologies, frameworks, trends in AI, ML, and cloud computing, evaluate emerging approaches, and lead strategic pilots or proofs-of-concept for next-generation solutions.

This role blends leadership in technology and systems architecture with hands-on expertise in cloud infrastructure, artificial intelligence, and machine learning, pivotal for driving innovation, scalability, and resilience in a modern enterprise.

Required Qualifications

• Bachelor's degree in Computer Science, Data Science, Information Systems, or a related field.
• Minimum of 5 years of hands-on data engineering experience using distributed computing approaches (Spark, Map Reduce, DataBricks)
• Proven track record of successfully designing and implementing cloud-based data solutions in Azure
• Deep understanding of data modeling concepts and techniques.
• Strong proficiency with database systems (relational and non-relational).
• Exceptional diagramming skills with tools like Visio, Lucidchart, or other data visualization software.

Preferred Qualifications

Additional Skills