Cloud AI Solutions Engineer

5-7 Years

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Early Applicant

Job Description

Job description:

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 CLIENT 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 CLIENT 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.

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

Education 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:

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.