Applied Ai Specialist-Cloud Fullstack

Your Role and Responsibilities:

As an Applied AI Specialist: Cloud FullStack, you will design, build, and evolve cloud-native and hybrid applications that translate client requirements into scalable, secure, and production-ready solutions. This role involves collaborating with digital workers and intelligent agents to deliver solutions, applying an AI-First mindset and following established methodologies.

Your primary responsibilities will include:

Solution Design: Designing and building cloud-native and hybrid applications that meet client requirements and are scalable, secure, and production-ready.

Application Development: Applying an AI-First mindset to automate scaffolding, dependency management, API definition, and validation, as well as database design and validation.

Pipeline Operation: Designing and operating CI/CD and GitOps pipelines with IaC and DevSecOps controls to ensure production readiness and cutover quality.

Quality Engineering: Performing unit, module, and functional testing using testing agents and synthetic data to ensure quality and reliability.

Incident Resolution: Analyzing logs and stack traces using AI agents to triage and resolve incidents and defects across different phases.

Required Education:

Bachelor's Degree

Preferred education

Master's Degree

Required Technical and Professional Expertise:

Cloud Application Development: Exposure to designing and building cloud-native and hybrid applications that meet client requirements and are scalable, secure, and production-ready.

AI-First Mindset: Experience applying AI-First mindset to automate scaffolding, dependency management, API definition, and validation, as well as database design and validation.

CI/CD Pipeline Operation: Exposure to designing and operating CI/CD and GitOps pipelines with IaC and DevSecOps controls to ensure production readiness and cutover quality.

Quality Assurance: Experience performing unit, module, and functional testing using testing agents and synthetic data to ensure quality and reliability.

Hyperscaler Platforms: Exposure to hyperscalers such as AWS, Azure, and Red Hat OpenShift, and deployment models to architect resilient and cost-optimized full-stack and integration patterns.

Preferred Technical and Professional Experience:

Additional AI Tools: Exposure to other AI tools and technologies can be beneficial.

Advanced Testing Methods: Familiarity with advanced testing methods and techniques is desirable.

Emerging Technologies: Knowledge of emerging technologies and trends in cloud and AI can be advantageous.

Years of Experience:
10 - 18