Azure Cloud Architect (Only Visa Independent Candidates)

Newlineinfo Corp - IT Services and IT Consulting; McLean (VA); Híbrido; USD 130.000 - 160.000; A tiempo parcial

Descripción de la vacante

A technology consulting firm is seeking a Cloud Architect in McLean, VA to design and optimize cloud-based data architecture. This role demands expertise in data engineering and Azure, with responsibilities including overseeing AI/ML pipeline development and selecting optimal technologies. Candidates should have at least 5 years of relevant experience and a Bachelor's degree in a related field. This is a contract position offering remote work options.

Formación

• 5+ years of hands-on data engineering experience with distributed computing approaches.
• Proven track record in designing cloud-based data solutions in Azure.
• Deep understanding of data modeling concepts and techniques.

Responsabilidades

• Design and optimize cloud-based data architecture.
• Oversee AI/ML pipeline development stages.
• Evaluate and select optimal cloud services and technologies.

Conocimientos

Data engineering

Cloud solutions design

Communication

Analytical problem-solving

Teamwork

Educación

Bachelor's degree in Computer Science or related field

Herramientas

Azure

Visio

SQL

Python

DataBricks

Descripción del empleo

Azure Cloud Architect (Only Visa Independent Candidates)

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Job Title: Cloud Architect

Location: McLean, VA (Remote work allowed)

Type: Contract

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.

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).
• 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.

Seniority level: Mid‑Senior level

Employment type: Contract

Job function: Information Technology

Industries: IT Services and IT Consulting