Azure Cloud Architect

Azure Cloud Architect With AI/ML

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

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