Cloud Architect&nbsp

Cloud Architect

Location: McLean, VA preferred ( else remote is OK)

Job Description:

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.