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

Overview

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.

Responsibilities

• 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.

Qualifications

• 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, JavaScript, .NET, 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, JavaScript, .NET, 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 BU’s 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.