Cloud AI Solutions Engineer||Remote||USC or GC or GC EAD or H4ead

[Adarsh Kumar]

Role: Cloud AI Solutions Engineer

Location: Remote   

Duration: 3–6-month contract

Visa – Gc/ USC/ GCEAD/ h4ead

Job Summary:

The Cloud AI Solutions Engineer will report to the Manager of Enterprise Generative AI in the Business Partnerships and Innovation Department within ITS. They will play a pivotal role in the technical advancement and implementation of high-performance AI solutions across major cloud platforms (e.g., Google Cloud/Vertex AI, Microsoft Azure AI, AWS, Oracle Cloud/OCI). This role is strictly technical and focuses on the backend architecture, coding, and deployment of sophisticated AI models. The Engineer is responsible for the technical realization of major projects like skAI and CPS ASSIST, ensuring they are scalable, secure, and integrated with district legacy systems. They will work in constant, close-knit collaboration with the Enterprise AI Design Specialist (who provides the user-facing logic and UI/UX requirements) and the Enterprise AI Program Specialist (who manages the operational rollout and stakeholder training) to deliver holistic, enterprise-grade AI products.

 

Principal Accountabilities:

• Technical Architecture & Backend Development: Design, develop, and deploy sophisticated AI models and applications, focusing on Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architectures.
• Cloud Infrastructure Management: Manage and optimize AI workloads across diverse cloud environments including Vertex AI, Azure AI, AWS, and OCI.
• System Integration: Develop secure APIs and microservices to integrate AI capabilities into existing district platforms such as Student Information Systems (SIS) and Learning Management Systems (LMS).
• Collaborative Implementation: Partner daily with the Enterprise AI Design Specialist to translate human-centered design prototypes into functional technical builds for skAI and CPS ASSIST.
• Operational Alignment: Coordinate with the Enterprise AI Program Specialist to ensure technical features align with the AI University roadmap and district-wide training capacities.
• Data Engineering for AI: Implement efficient data retrieval mechanisms, manage vector databases, and maintain knowledge graphs to support AI accuracy and performance.
• Security & Scalability: Utilize Infrastructure as Code (IaC) and CI/CD pipelines to ensure AI deployments are automated, reproducible, and strictly follow district security and privacy standards.
• Facilitate Human-Centered Co-Design: Lead the Co-Design Initiative, partnering with teachers and students in UX focus groups to ensure all AI modules are culturally responsive, practically relevant, and pedagogically sound.
• Automate Workflow Solutions: Design and implement functional "Gems" (custom Gemini models) and NotebookLM instances for Central Office departments to automate repetitive administrative tasks and improve data retrieval.
• Maintain the Instructional Knowledge Graph: Oversee the Instructional Knowledge Graph, ensuring AI competencies are mapped across core academic standards and proactively updating materials as technology evolves.
• Ensure Ethical Design Integrity: Apply the CPS AI Principles (Equitable, Transparent, Human-Centered, Sustainable) and AI Readiness Framework to all designs, prioritizing accessibility for diverse learners and English Learners.
• Integrated AI Literacy Modules: A "Gen Ed" AI curriculum fully ingested into the Skyline platform for universal district access.
• Enhanced Skyline User Profiles: Technical build-out of "Teacher-as-Learner" and "Parent-as-Learner" capabilities to support role-specific tracking and personalized learning pathways.
• AI Readiness Dashboard: A public-facing scoring engine and data visualization tool used to report school-level AI literacy Digital Credentialing Hub: A technical solution for deploying a district AI badge program with portable, open-standard credentials.
• Automated Data Collection Workflows: Integrated systems within the data warehouse to ingest existing data for real-time readiness reporting.

 

In order to be successful and achieve the above responsibilities, the Cloud AI Solutions Engineer must possess the following qualifications:

 

Education Required:

• Bachelor's Degree from an accredited college or university in Computer Science, Information Technology, or a related field is required.

 

Experience and Number of Years:

• A minimum of five (5) years of professional experience in AI development, cloud computing, or software engineering.
• Hands-on experience building and deploying Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems.
• Proven experience with major cloud platforms such as Google Cloud (Vertex AI), Microsoft Azure, AWS, or Oracle OCI.
• Experience with containerization (Docker) and orchestration (Kubernetes) for scaling AI applications.

 

Preferred Certifications:

• Microsoft Azure AI Engineer Associate (AI-102)
• Microsoft Azure Solutions Architect Expert (AZ-305)
• AWS Certified Machine Learning – Specialty
• AWS Certified Solutions Architect – Professional
• Google Cloud - Professional Machine Learning Engineer
• Google Cloud - Professional Cloud Architect
• Certified Kubernetes Administrator (CKA)
• Salesforce Certified AI Administrator
• TOGAF Certification

 

Knowledge, Skills, and Abilities:

• Deep Technical Expertise: Proficiency in Python and AI frameworks such as TensorFlow, PyTorch, or LangChain.
• Backend Mastery: Strong understanding of microservices architecture, web services, and API development for AI system integration.
• Data Infrastructure: Skill in managing vector databases and implementing ETL processes for AI training data.
• Cloud Agnostic Logic: Ability to design solutions that can operate seamlessly across a multi-cloud environment.
• Communication: Ability to explain complex technical backend issues to the Design and Program Specialists to ensure collective project success.

 

 

Regards,

Adarsh