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EXL

Cloud Architect – Application Development

EXL

Cloud Architect leading the design and deployment of machine learning applications. Involved in innovation and collaboration across cross-functional teams to drive operational excellence.

Posted 7/9/2026full-timePasay • 🇵🇭 PhilippinesSeniorLeadWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in architecting scalable data science and machine learning pipelines using AWS services, while managing multi-tenant systems and CI/CD processes. Proven ability to lead cross-functional teams and translate technical requirements into effective solutions that align with business objectives.

Highest-signal resume keywords
AWS Cloud ServicesMLOpsCI/CD Pipeline ManagementData Science Application DevelopmentMulti-Tenant SaaS Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Data ScienceMachine LearningSoftware EngineeringCloud ArchitectureApplication DeploymentImpact AssessmentTechnical ArchitecturePipeline ManagementDevOps PracticesSaaS Platforms
Soft Skills
LeadershipCommunicationStakeholder ManagementMentoringCollaboration
Tools & Technologies
AWS SageMakerAWS TextractAWS ComprehendCI/CD ToolsDevOps Tools
Industry Keywords
Data Science PipelinesMulti-Tenant SystemsCloud EnvironmentsTechnical AdvisorProduct Innovation

Tech Stack

Tools & technologies
AWSCloud

About the role

Key responsibilities & impact
  • Architect scalable, reusable data science and machine learning pipelines leveraging cloud services such as AWS SageMaker, Textract, and Comprehend.
  • Design multi-tenant systems with robust application deployment architectures and rate-limiting mechanisms to ensure high availability and performance.
  • Translate custom client requirements into scalable technical architecture, conducting impact assessments and delivery planning.
  • Lead development and deployment of complex data science and information extraction solutions in client cloud environments.
  • Manage and optimize CI/CD pipelines for continuous delivery of data science applications.
  • Own the tenant onboarding process and manage lifecycle operations for multi-tenant deployments.
  • Oversee DevOps and MLOps teams to ensure reliable, consistent releases and maintenance of deployed products.
  • Collaborate with internal and client DevOps teams to implement best practices and streamline application-specific DevOps workflows.
  • Monitor and enhance system performance and operational stability through effective pipeline and infrastructure management.
  • Drive collaboration between engineering, data science, delivery, and leadership teams to align technical solutions with business goals.
  • Mentor and manage software engineers, DevOps, and MLOps personnel to foster a culture of continuous improvement.
  • Represent the technical architecture team in leadership discussions, providing insights and recommendations for strategic decision-making.
  • Act as a key technical advisor during client engagements, translating technical complexities into clear business value propositions.
  • Support internal business initiatives, including technical demonstrations, competitions, and strategic presentations.
  • Continuously gather feedback to iterate on solutions and drive product innovation.

Requirements

What you’ll need
  • Bachelor’s degree in computer science, Information Technology, Business Administration, or related field.
  • 7+ years of experience spanning software engineering, data science, and DevOps roles.
  • Proven expertise in cloud platforms (AWS preferred), MLOps, and CI/CD pipeline design and management.
  • Strong programming skills and hands-on experience with data science application development and deployment.
  • Experience managing multi-tenant SaaS or ML platforms.
  • Strong leadership, communication, and stakeholder management skills.

Benefits

Comp & perks
  • Technical Architect with a strong background in software engineering, data science, and DevOps to lead the design, development, and deployment of scalable machine learning and data science applications.