Intern (Technical Internship) | Software, Analytics & AI for Property Management Operations

The Technical Intern will support business transformation initiatives by contributing to project-based solutions in areas such as artificial intelligence (AI) automation data analytics quality assurance (QA) automation software development and citizen development under close mentorship and established business guidelines. This internship pathway is focused on technical support enablement knowledge management and operations engineering providing students with hands-on experience in solving real business challenges through technology.

The intern will begin by analyzing an identified business problem understanding current workflows and assessing operational pain points. Working closely with a designated mentor and business owner the intern will design develop test and document practical solutions that deliver measurable value. Projects may include the creation of Python or JavaScript scripts Power Automate workflows Power BI dashboards data validation tools API or test automation prototypes AI-assisted quality or knowledge management workflows technical content pipelines search evaluation methods or other approved reusable tools.

This internship is designed for students who can apply technical skills in a business environment while following defined governance security and quality standards. All work outputs are expected to include proper documentation knowledge transfer and human review particularly for AI-assisted solutions ensuring responsible use and clear understanding of limitations. The role provides exposure to real-world technical problem solving cross-functional collaboration and the development of scalable solutions that support business operations and continuous improvement.

• Work with the mentor and business owner to define the problem statement intended users current process pain points project boundaries and measurable success criteria.
• Review approved source data workflows technical documentation support cases knowledge content or performance measures needed to understand the problem.
• Design a small testable technical approach using approved programming automation analytics AI API or testing tools appropriate to the assignment.
• Develop and iterate on scripts workflows dashboards data-validation routines QA aids content tools integrations or AI-assisted prototypes under mentor review.
• Use version control naming conventions structured files comments change notes or other approved methods to make technical work traceable and maintainable.
• Create test cases and validate functionality accuracy edge cases error handling data quality and expected user behavior before any broader use.
• For AI-assisted work design prompts or workflows validate outputs against trusted source evidence identify hallucination or bias risks and require human approval for decisions or published content.
• Use only approved tools accounts devices data environments and access levels; protect confidential information and avoid exposing production or personal data unnecessarily.
• Collaborate with support knowledge product engineering quality training data operations information security or other technical partners as assigned.
• Document the solution architecture or logic data inputs operating steps user guide test results known limitations support boundary and rollback or escalation path.
• Demonstrate progress in regular mentor reviews and communicate blockers risks scope changes and technical decisions clearly to both technical and non-technical stakeholders.
• Measure the prototype against agreed outcomes such as time saved fewer errors improved search success reduced repetitive work better resolution quality or increased adoption.
• Prepare a final demo and handoff package train the named owner where appropriate and confirm who will maintain or retire the output after the internship.
• Do not deploy changes to production publish AI-generated content or automate high-risk decisions without the approvals and controls defined by the BU and relevant corporate functions.

• Currently enrolled in computer science information technology information systems computer engineering software engineering data science artificial intelligence business analytics applied mathematics statistics information management library and information science or a related program.
• Demonstrable technical proof through coursework a capstone GitHub or other repository automation demo dashboard hackathon research project technical article test project or equivalent portfolio evidence.
• Foundational programming or automation exposure in Python Power Automate or an equivalent language/platform.
• Basic understanding of SQL structured data CSV/Excel handling APIs Git or version control and data validation; depth may vary by project.
• Exposure to Power BI test automation workflow automation AI/LLM tools prompt design retrieval concepts or cloud platforms is an advantage.
• Ability to break a problem into smaller steps test assumptions debug methodically and document evidence rather than rely on unsupported conclusions.
• Clear communication and the ability to explain technical concepts trade-offs limitations and user instructions to non-technical stakeholders.
• Responsible approach to privacy security intellectual property access controls and human review when using data code or AI tools.
• Ability to receive code design content or process feedback and revise work through short testable iterations.
• Ability to comply with the assigned hybrid schedule project milestones mentor cadence approved technology stack and internship-hour limits.

Sub-Qualifications

• Provides technical proof in at least one programming or automation area such as Python Power Automate or equivalent coursework and project work.
• Can work with structured data and demonstrates basic SQL Excel/CSV handling data validation APIs Git Power BI or similar tooling appropriate to the project.
• Understands basic AI prompt design source grounding output validation and the need for safe responsible use and human review.
• Can clarify a manual pain point map the current process identify the user and decision points and define a small prototype rather than attempt an uncontrolled large solution.
• Can build test and refine a small script dashboard workflow AI-assisted QA tool knowledge-retrieval aid API test automation or integration with mentor guidance.
• Can create test cases validate edge conditions document errors and explain how accuracy and reliability were assessed.
• Can use version control or equivalent change tracking and write clear technical documentation user instructions handoff notes support boundaries and maintenance steps.
• Can explain privacy security access data-quality and hallucination risks relevant to the proposed solution and follow the BUs guardrails.
• Can present a technical demo to business stakeholders receive feedback and translate it into a prioritized next iteration.
• Can define or help track an adoption or impact measure such as hours saved cycle-time reduction fewer errors improved quality search success or user uptake.
• Understands that Type 3 work must be conducted in an approved sandbox read-only test or otherwise controlled environment unless specific production approval is granted.
• Meets the Type 3 gate: real project named mentor business owner measurable deliverable approved tools/data human review for AI documented handoff and maintenance owner and a plausible conversion or absorption path.