Job Description

Role: Cloud Architect

Duration: 6-12+ Months Contract

Location: San Jose, CA - Onsite

Note: This role is no longer open for remote work. The candidate will be required to work out of the client office in San Jose, California or Lehi, Utah. This is a hybrid position, with on-site presence.

Notes: AI domain experience not required, but a strong understanding of architectural support for AI initiatives is a plus. This is a data-focused architecture role, not a solutions architect or infrastructure admin position.

Mandatory Requirements:

• Non-CS degrees, BCA, or 3-year programs will not be considered.
• Cloud Expertise: Hands-on experience with Azure or AWS (either or both). Google Cloud Platform-only experience is insufficient.
• Enterprise Diagramming & Modeling : Strong experience creating enterprise-wide data models and system diagrams.

Must Tools: Lucidchart or Microsoft Visio

Ideal candidate: Proficiency in Spark programming Databricks experience is a big plus. Experience in data architecture for large enterprises. Solid track record as an individual contributor, ideally growing from developer to architect. Cloud certifications (Azure/AWS) are highly desirable.

Client Job Description: The Cloud Architect will be a key contributor to designing, evolving, and optimizing our company''s cloud-based data architecture. This role requires a strong background in data engineering, hands-on experience building cloud data solutions, and a talent for communicating complex designs through clear diagrams and documentation. Must work EST hours.

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:

• Advanced knowledge of cloud-specific data services (e.g., DataBricks, Azure Data Lake).
• Expertise in big data technologies (e.g., Hadoop, Spark).
• Strong understanding of data security and governance principles.
• Experience in scripting languages (Python, SQL).

Additional Skills:

• Communication: Exemplary written and verbal communication skills to collaborate effectively with all teams and stakeholders.
• Problem-solving: Outstanding analytical and problem-solving skills for complex data challenges.
• Teamwork & Leadership: Ability to work effectively in cross-functional teams and demonstrate potential for technical leadership.

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