Associate Engineer, AI Solutions

Think Together•Santa Ana, CA

1d•$95,000 - $105,000•Onsite

About The Position

Associate AI Solutions Engineer Founded in 1997, Think Together partners with schools to change the odds for kids by providing award-winning programs during and outside the school day. We’re one of California’s largest nonprofits working in school districts from San Diego to San Francisco. Whether you’re interested in early learning, elementary, middle or high school, you can invest in your community by working at a local school or working within our home office providing support! JOB SUMMARY Reporting to the Leader, AI Transformation & Strategic Projects, the AI Solutions Engineer advances promising AI Champion discoveries from validated concept into secure, functional, supportable solutions that can scale across Think Education and Think Together. Working under general direction, this role builds, integrates, tests, deploys, monitors, and supports AI agents, copilots, retrieval solutions, and workflow automations using approved enterprise platforms and established architecture, security, privacy, accessibility, and responsible AI practices. The role partners with the AI Business Process & Automation Analyst, AI Champions, cybersecurity, architecture, platform owners, service teams, and business process owners to translate approved requirements into reliable technical outcomes. It creates the documentation, evaluation evidence, operational controls, and knowledge transfer needed for safe production use. This position includes both “build” and “run” IT service duties. Build duties are the addition of new systems, features, and functions for both continuous improvement and to support Think Education’s growth. Run duties are the on-going support of the Think Education systems, applications, data environments, and processes to ensure they are meeting users’ needs. The amount of effort dedicated to build and run duties will fluctuate from time to time depending on the needs of the business.

Requirements

Bachelor’s degree in computer science, software engineering, information systems, data science, or a related field preferred, or an equivalent combination of relevant experience, certifications, coursework, and demonstrated applied learning.
Two or more years of relevant experience in software development, automation, data, integration, application support, cloud platforms, or a related technical discipline, including experience contributing to the delivery or support of production systems. Relevant internal experience and a demonstrated portfolio may substitute for direct AI job experience.
Demonstrated ability to build or configure at least one working automation, integration, agent, copilot, retrieval solution, or similar technical solution; connect it to an Application Programming Interface (API), data source, or enterprise system; and explain how it was tested, deployed, monitored, and supported.
Working knowledge of generative AI concepts, prompt design, retrieval augmented generation, agent workflows, model limitations, human oversight, and at least one scripting or programming language such as Python, JavaScript, TypeScript, PowerShell, or SQL, together with source control, structured testing, release and change controls, monitoring, troubleshooting, security, privacy, accessibility, records, and responsible AI practices.
Must pass Live Scan (criminal background check via fingerprinting).
Provide and maintain a clear TB test throughout employment.
Reliable transportation is required to meet the needs of the position.
While driving may not be a regular requirement, employees who operate a personal or company vehicle related to work location requirements must maintain, and provide Human Resources, proof of a valid driver’s license and automobile insurance.

Nice To Haves

Experience with Microsoft 365, SharePoint, Teams, CRM, service management, or related enterprise platforms.
Experience advancing validated pilots into production-ready solutions.
Experience with cloud-based AI services, Continuous Integration/Continuous Delivery, observability, and release management.
Experience building workflows and/or agentic solutions.
Experience mentoring and supporting non-technical practitioners.

Responsibilities

Design and deploy scalable, production-ready AI agents using approved enterprise platforms, architectures, and development standards.
Develop and evaluate permission-aware Retrieval Augmented Generation (RAG) solutions using organizational knowledge repositories.
Integrate AI capabilities with enterprise systems and platforms (e.g., Teams, CRM) to ensure secure data access and AI-enabled business processes.
Apply context-engineering and harness engineering practices to improve solution quality, reliability and maintainability.
Build the orchestration, evaluation, guardrails, observability, and feedback mechanisms required to operate AI solutions safely at scale.
Develop and implement AI testing frameworks that access model quality, security, safety, reliability, performance, accessibility, and cost across the solution lifecycle.
Document evaluation results, residual risks, acceptance decisions, and human oversight requirements before release to inform decisions and ensure AI solutions are ready for safe and responsible deployment.
Configure and maintain AI platforms, access controls, and governance mechanisms to ensure secure, compliant, and scalable AI operations.
Partner with cybersecurity and enterprise architecture teams to shape AI solution designs.
Operationalize applicable AI governance standards by translating them into practical requirements and controls that enable responsible and scalable AI solutions.
Implement environment separation, source control, peer review, automated testing, deployment approvals, versioning, release documentation, and rollback capabilities.
Manage monitoring for solution quality, availability, latency, consumption, cost, security events, model or data drift, and control failures; identify issues and drive timely remediation.
Establish governed knowledge-ingestion and retrieval practices covering authoritative sources, ownership, permissions, freshness, retention, deletion, and lineage.
Manage AI incidents, problems, vulnerabilities, vendor and model changes, and corrective actions in coordination with cybersecurity and service-management teams.
Define continuity, recovery, fallback, and graceful-degradation approaches for business-critical AI capabilities.
Own the technical path from feasibility assessment through engineering, production readiness, release, and operation, partnering with the AI Business Process & Automation Analyst.
Conduct documented technical feasibility, architecture, effort, dependency, operating cost, and risk assessments before pilots or production commitments are approved.
Document technical architectures and operating procedures.
Provide or coordinate Tier 3 support for AI solutions.
Mentor AI Champions and Technologists.
Provide technical training to business analysts and solution owners.
Establish reusable design patterns and development standards.