Cloud Data Architect (AI/ML)...............Remote, working in EST

Cloud Data Architect (AI/ML)

Location: Remote, working in EST

Duration: 12 months

Top Skills' Details

• Must have a Bachelor's degree in Computer Science
• Must have 5+ years of hands-on experience using distributed computing architecture
• Significant hands-on experience in cloud architecture (e.g., AWS, Azure, Google Cloud Platform), big data platforms, and end-to-end data solution workflows
• Demonstrated expertise in machine learning/AI frameworks, platforms, and MLOps
• Proven record of building, optimizing, and documenting scalable cloud, data, and ML solutions.
• Ability to diagram architectures at multiple levels of detail, ranging from conceptual to detailed implementation.
• Ability to document every aspect of work, including technical decisions, architectures, workflows, and operational procedures
• Strong skills in technical communication, documentation, and cross-functional collaboration.

Note:

This contract role is strictly for individual contributors, not involving management of staff. Collaboration with internal teams is required, but the primary focus is on hands-on, independent solution delivery for enterprise-grade data and AI/ML projects.

* Manager requires College Degree (including school, degree, and years attended) to be clearly stated on resumes

**MUST work in EST time zone**

Secondary Skills - Nice to Haves

Job Description

This Cloud Data Architect resource will work hands-on to deliver scalable, secure, and high-performance systems for analytics, reporting, and machine learning on a petabyte scale.

CORE RESPONSIBLITIES

CLOUD DATA ARCHITECTURE DESIGN & IMPLEMENTATION

- Must be capable of architecting and building scalable, secure cloud-based platforms for analytics, reporting, data lakes, warehouses, and machine learning workloads.

- Should be able to deliver systems supporting both real-time and batch analytics.

AI/ML PIPELINE DEVELOPMENT

- Required to create, deploy, and monitor end-to-end AI/ML solutions, encompassing data preprocessing, modeling, inference, and solution improvement.

- Must automate workflows using MLOps, CI/CD, and orchestration tools, ensuring experimentation, scalability, and operational efficiency.

DATA MODELING, INTEGRATION, & QUALITY

- Must deliver robust data models, metadata, and taxonomy strategies suitable for large, distributed data architectures.

- Expected to support and maintain high-quality data integration, lineage, and lifecycle management processes.

TECHNOLOGY EVALUATION & SOLUTIONING

- Responsible for researching, recommending, and implementing appropriate technologies related to cloud, big data, analytics, and ML.

- Must be able to lead proofs-of-concept and pilot implementations.

SECURITY, PRIVACY, & COMPLIANCE

- All work must adhere to data security, privacy, and compliance standards.

- Must coordinate with relevant teams to meet regulatory benchmarks and ensure secure, compliant solutions.

TECHNICAL COLLABORATION & DOCUMENTATION

- Must collaborate effectively with engineering, data science, IT, and business stakeholders for requirements gathering and solution delivery.

- Responsible for producing clear technical documentation, diagrams, and supporting artifacts.

SYSTEM OPTIMIZATION & TROUBLESHOOTING

- Must monitor and maintain optimal performance and cost-effectiveness of delivered solutions; required to troubleshoot and resolve issues proactively.

CONTINUOUS LEARNING & INNOVATION

- Expected to remain informed about current trends in cloud, data, and AI/ML and to recommend architectural improvements based on industry best practices.