Compunnel, Inc.
The Cloud Architect will play a pivotal role in designing, evolving, and optimizing our cloud-based data architecture.
This position requires strong expertise in data engineering, hands‑on experience building cloud data solutions, and the ability to communicate complex designs through clear diagrams and documentation.
The role operates in EST hours.
Key Responsibilities
Strategy & Planning: Align AI and ML system design with business objectives, shaping technology roadmaps and architectural standards for cloud‑driven analytics and AI adoption. AI/ML Workflow Design: Architect and oversee end‑to‑end AI/ML pipelines, including data ingestion, preprocessing, model training, validation, deployment, monitoring, and lifecycle management within cloud environments. Technology Selection: Evaluate and select optimal cloud services, AI/ML platforms, infrastructure components, frameworks, and tools based on operational, financial, and security requirements. Infrastructure Scalability: Design and scale distributed cloud solutions for real‑time and batch processing workloads, leveraging Kubernetes, managed ML platforms, and hybrid/multi‑cloud strategies. MLOps & Automation: Implement automated build, test, and deployment pipelines for machine learning models, enabling continuous delivery and rapid prototyping. Security & Compliance: Establish protocols for data access, privacy, encryption, and regulatory compliance (e.g., GDPR, ethical AI), coordinating with security teams to mitigate risks. Collaboration: Act as a liaison between business stakeholders, development teams, and data scientists to translate requirements into technical solutions. Performance Monitoring: Monitor infrastructure and AI workloads, optimize resource allocation, troubleshoot bottlenecks, and fine‑tune models for reliability and cost‑efficiency. Documentation: Maintain architectural diagrams, policy documentation, and knowledge bases for AI/ML and cloud infrastructure. Continuous Innovation: Stay current with emerging technologies and trends in AI, ML, and cloud computing; lead pilots and proofs‑of‑concept for next‑generation solutions. Required Qualifications
Bachelor’s degree in Computer Science, Data Science, Information Systems, or related field. Minimum 5 years of hands‑on data engineering experience using distributed computing approaches (e.g., Spark, MapReduce, Databricks). Proven experience designing and implementing cloud‑based data solutions in Azure. Strong understanding of data modeling concepts and techniques. Proficiency with relational and non‑relational database systems. Exceptional diagramming skills using tools like Visio, Lucidchart, or similar. Preferred Qualifications
Advanced knowledge of cloud‑specific data services (e.g., Databricks, Azure Data Lake). Strong understanding of data security and governance principles. Experience with scripting languages (Python, SQL).
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Strategy & Planning: Align AI and ML system design with business objectives, shaping technology roadmaps and architectural standards for cloud‑driven analytics and AI adoption. AI/ML Workflow Design: Architect and oversee end‑to‑end AI/ML pipelines, including data ingestion, preprocessing, model training, validation, deployment, monitoring, and lifecycle management within cloud environments. Technology Selection: Evaluate and select optimal cloud services, AI/ML platforms, infrastructure components, frameworks, and tools based on operational, financial, and security requirements. Infrastructure Scalability: Design and scale distributed cloud solutions for real‑time and batch processing workloads, leveraging Kubernetes, managed ML platforms, and hybrid/multi‑cloud strategies. MLOps & Automation: Implement automated build, test, and deployment pipelines for machine learning models, enabling continuous delivery and rapid prototyping. Security & Compliance: Establish protocols for data access, privacy, encryption, and regulatory compliance (e.g., GDPR, ethical AI), coordinating with security teams to mitigate risks. Collaboration: Act as a liaison between business stakeholders, development teams, and data scientists to translate requirements into technical solutions. Performance Monitoring: Monitor infrastructure and AI workloads, optimize resource allocation, troubleshoot bottlenecks, and fine‑tune models for reliability and cost‑efficiency. Documentation: Maintain architectural diagrams, policy documentation, and knowledge bases for AI/ML and cloud infrastructure. Continuous Innovation: Stay current with emerging technologies and trends in AI, ML, and cloud computing; lead pilots and proofs‑of‑concept for next‑generation solutions. Required Qualifications
Bachelor’s degree in Computer Science, Data Science, Information Systems, or related field. Minimum 5 years of hands‑on data engineering experience using distributed computing approaches (e.g., Spark, MapReduce, Databricks). Proven experience designing and implementing cloud‑based data solutions in Azure. Strong understanding of data modeling concepts and techniques. Proficiency with relational and non‑relational database systems. Exceptional diagramming skills using tools like Visio, Lucidchart, or similar. Preferred Qualifications
Advanced knowledge of cloud‑specific data services (e.g., Databricks, Azure Data Lake). Strong understanding of data security and governance principles. Experience with scripting languages (Python, SQL).
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