Tech Rakers

Compensation

Full Description

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.