Cloud Architect – Data & AI Architecture Focus

$78 / hr on C2C

Onsite- 5 days..

San Jose, California or Lehi, Utah

Glider

Azure Cloud Data Solutions (Data Lake, Databricks, distributed systems) 5+ yrs

AI / ML Architecture (end-to-end pipeline : ingestion training deployment monitoring)

DataEngineering (Spark, Hadoop, MapReduce)

DataModeling + DB Systems (relational + NoSQL)

Cloud Architect Data & AI Architecture Focus

Immediate Replacement

Manager is seeking an experienced Cloud Architect to take over responsibilities from a recently completed engagement.

This is not an infrastructure or admin role the ideal candidate is a hands-on architect with strong expertise in data ingestion, ETL processes, and designing architectures to support AI initiatives.

AI experience is not mandatory, but a perspective on AI use cases is expected.

Role Overview

Design and define enterprise data architectures for large-scale systems. Lead data ingestion, transformation (ETL), and modeling for cloud-based platforms. Architect foundational infrastructure for future AI / ML capabilities. Create detailed architecture diagrams and enterprise data models using Lucidchart or Visio.

Mandatory Requirements :

Educational : Must hold a Bachelor's degree (B.Tech) in Computer Science & Engineering (4-year program).

Non-CS degrees, BCA, or 3-year programs will not be considered.

Cloud Expertise : Hands-on experience with Azure or AWS (either or both). GCP-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) highly desirable.

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.

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.