Cloud Architect – Data & AI Architecture Focus

The Cloud Architect will be a key contributor to designing evolving and optimizing our companys 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

Bachelors 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.