AI Use Case Prototyping Intern

📍 Job Overview

Job Title: AI Use Case Prototyping Intern

Company: Unisys

Location: Bangalore, Karnataka, India

Job Type: Full time, Intern

Category: AI/ML & Data Operations

Date Posted: August 14, 2026

Experience Level: 0-2 years

Remote Status: On-site

🚀 Role Summary

• Engage in the development and testing of Proofs of Concept (PoCs) for enterprise AI use cases, focusing on areas like incident management and service desk automation.

• Design, build, and refine agentic workflows, incorporating task chaining, reasoning steps, and automation triggers for enhanced operational efficiency.

• Utilize prompt tuning techniques to evaluate AI model outputs and systematically improve response quality and accuracy.

• Develop comprehensive documentation for AI prototypes, ensuring seamless transition and implementation by engineering teams.

• Analyze system logs and identify patterns to inform and improve the prediction models and overall AI system performance.

📝 Enhancement Note: This role is positioned as an intern level, focusing on practical application and learning within the AI and Data Operations domain. The emphasis is on hands-on prototyping and understanding AI workflows, rather than deep strategic or long-term operational planning. The core function is building and testing immediate AI solutions.

📈 Primary Responsibilities

• Develop and test Proofs of Concept (PoCs) for enterprise AI use cases, specifically targeting incident management and service desk automation.

• Create and iterate on agentic workflows, including defining task sequences, implementing reasoning capabilities, and setting up automation triggers.

• Conduct prompt tuning experiments to enhance the quality, relevance, and accuracy of AI model outputs.

• Document all developed prototypes thoroughly, ensuring clarity for knowledge transfer to engineering and development teams.

• Analyze system logs and operational data to identify trends, anomalies, and areas for improvement in AI prediction models.

• Assist in data collection, cleaning, and analysis as required for AI model training and evaluation.

• Contribute to the testing of software for bugs and participate in code reviews, learning version control and secure coding practices.

• Support general project coordination and progress tracking for AI initiatives.

• Compile and organize information for reports and presentations related to AI use case development.

📝 Enhancement Note: The primary responsibilities outline a hands-on, technical internship focused on the practical development and testing of AI prototypes. This includes a strong emphasis on workflow design, prompt engineering, and data analysis, all geared towards improving specific enterprise functions like incident and service desk management.

🎓 Skills & Qualifications

Education: Currently pursuing a B.Tech degree in Computer Science, Information Science, or Artificial Intelligence/Machine Learning (AI/ML).

Experience: Generally, less than 1 year of experience in a relevant technical discipline.

Required Skills:

• Familiarity with Artificial Intelligence (AI) concepts and their practical applications.

• Basic understanding of Prototyping methodologies for software or AI solutions.

• Exposure to Software Development principles and practices.

• Foundational knowledge in Data Analysis and the ability to interpret data.

• Conceptual understanding of Agentic Workflows and their potential in automation.

• Awareness of Prompt Tuning techniques for AI model interaction.

• Proficiency in at least one scripting language, such as Python, for automation and tool development.

• Understanding of Continuous Integration/Continuous Deployment (CI/CD) principles.

• Basic knowledge of Cloud Infrastructure concepts.

• Aptitude for Technical Support and troubleshooting.

• Familiarity with System Configuration tasks.

• Ability to create basic reports and visualizations, potentially using tools like Excel or PowerPoint.

• Understanding of Version Control systems (e.g., Git).

• Knowledge of Secure Coding practices. Preferred Skills:

• Experience with developing AI use cases in areas like incident management or service desk automation.

• Hands-on experience in creating and refining agentic workflows.

• Practical application of prompt tuning for AI response optimization.

• Experience analyzing system logs and improving prediction models.

• Familiarity with data cleaning and preparation techniques for AI models.

• Exposure to DevSecOps principles and tools.

• Experience with networking and infrastructure support tasks.

• Understanding of data visualization tools beyond basic reporting.

📝 Enhancement Note: The required skills are typical for an intern with an academic background in CS/AI/ML, emphasizing foundational knowledge and a willingness to learn. Preferred skills indicate areas where practical exposure, even from academic projects, would be highly beneficial and demonstrate initiative.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase of academic projects or personal initiatives demonstrating AI use case prototyping.

• Examples of agentic workflows designed or conceptualized, detailing task chaining and automation logic.

• Demonstrations of prompt tuning experiments, illustrating improvements in AI response quality.

• Documentation samples for prototypes, highlighting clarity and technical detail for engineering handover.

• Evidence of data analysis and reporting capabilities, potentially through project reports or dashboards. Process Documentation:

• Ability to document the design and implementation phases of AI prototypes.

• Experience in outlining workflow designs and optimization strategies for AI-driven processes.

• Understanding of how to measure and analyze the performance of AI models and prototypes.

📝 Enhancement Note: For an intern role, the "portfolio" will likely consist of academic projects, coding assignments, or personal AI exploration projects. The emphasis is on demonstrating the ability to understand and apply AI concepts, document work clearly, and show potential for growth in process-oriented tasks.

💵 Compensation & Benefits

Salary Range: As this is an internship position, compensation will be in line with industry standards for AI/ML interns in Bangalore, India. Based on typical internship stipends for B.Tech students in this region, a range of ₹15,000 to ₹30,000 per month is estimated.

Benefits:

• Opportunity to gain hands-on experience in cutting-edge AI use case prototyping.

• Mentorship from experienced AI and engineering professionals at Unisys.

• Exposure to enterprise-level AI challenges and solutions.

• Learning opportunities in areas such as agentic workflows, prompt tuning, and data analysis.

• Potential for networking within the technology sector.

• Contribution to real-world AI projects within a global IT services company.

Working Hours: Typically 40 hours per week, Monday to Friday, aligning with standard business hours in India (Asia/Kolkata timezone). Flexibility may be offered based on academic schedules and project needs, subject to approval.

📝 Enhancement Note: Salary is estimated based on general internship compensation trends in Bangalore for technology roles. The benefits focus on learning, development, and exposure, which are key attractors for intern positions.

🎯 Team & Company Context

🏢 Company Culture

Industry: Information Technology Services and Consulting. Unisys is a global provider of IT services, focusing on helping clients to leverage technology to achieve their business objectives. This includes areas like digital workplace, cloud and infrastructure, and enterprise computing solutions.

Company Size: Unisys is a large enterprise, with thousands of employees globally. This means interns can expect exposure to structured processes, corporate policies, and a diverse workforce.

Founded: Unisys was founded in 1986 through the merger of Burroughs Corporation and Sperry Corporation. Its long history in the IT industry signifies stability and deep expertise.

Team Structure:

• The intern will likely be part of a dedicated AI or innovation team, possibly within a broader Engineering or R&D division.

• Reporting will be to a designated mentor or team lead who oversees intern projects.

• Collaboration will occur with other interns, junior engineers, and senior AI specialists on specific project tasks. Methodology:

• The team likely employs agile methodologies for project management and development.

• Emphasis will be on rapid prototyping, iterative testing, and data-driven decision-making.

• Collaboration tools and platforms will be used for communication, code sharing, and documentation.

Company Website: https://www.unisys.com/

📝 Enhancement Note: Unisys's background as a large IT services company suggests a structured environment with established processes. The AI team likely focuses on practical, client-facing solutions, making the intern's role directly relevant to business outcomes.

📈 Career & Growth Analysis

Operations Career Level: This role is an entry-level internship, designed for students to gain foundational experience in AI and Data Operations. It's an exploratory phase focused on skill acquisition and practical application of academic knowledge.

Reporting Structure: The intern will report to an assigned mentor or team lead, who will provide guidance, feedback, and project direction. This structure is typical for internships, ensuring structured learning and support.

Operations Impact: While direct operational impact is limited due to the intern capacity, the work on PoCs and prototypes contributes to the exploration and potential future implementation of AI solutions that can optimize Unisys's or its clients' operations. The analysis of logs and improvement of prediction models directly supports the enhancement of AI system efficiency.

Growth Opportunities:

• Skill Development: Deepen understanding and practical skills in AI, machine learning, prompt engineering, data analysis, and Python scripting.

• Industry Exposure: Gain insights into how AI is applied in enterprise settings for IT services and consulting.

• Networking: Build professional connections with AI practitioners, engineers, and mentors within Unisys.

• Potential for Future Roles: Successful completion of the internship may lead to opportunities for future roles or extended internships within Unisys, depending on performance and business needs.

📝 Enhancement Note: As an internship, the primary growth is educational and experiential. The role is a stepping stone, providing foundational skills and exposure that can lead to more specialized roles in AI/ML engineering or data science.

🌐 Work Environment

Office Type: On-site at Unisys's RGA Tech Park facility in Bangalore, India. This implies a corporate office environment.

Office Location(s): Bangalore, KA, India. This location is a major technology hub in India, offering a vibrant professional ecosystem.

Workspace Context:

• The workspace will be an office environment equipped with standard IT infrastructure.

• Access to necessary software, tools, and potentially specialized hardware for AI development will be provided.

• Opportunities for collaboration with other interns and team members will be available through designated meeting spaces and team interactions.

Work Schedule: Standard full-time work schedule (approximately 40 hours/week) during business days. While on-site, there might be some flexibility offered for academic commitments, subject to team and project requirements.

📝 Enhancement Note: The on-site requirement in a major tech park suggests a professional, collaborative office setting conducive to learning and team interaction, with access to company resources.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Review of application, resume, and academic background to assess fit for the internship requirements.

• Technical Assessment: May include coding challenges, problem-solving questions related to AI, data analysis, or Python scripting, and discussions on academic projects.

• Behavioral Interview: Assessment of soft skills, communication, problem-solving approach, and cultural fit with Unisys.

• Mentor/Team Lead Interview: Discussion about specific project interests, understanding of AI use cases, and expectations for the internship.

Portfolio Review Tips:

• Highlight Relevant Projects: Focus on academic projects, personal AI experiments, or coding assignments that showcase your understanding of AI concepts, prototyping, data analysis, or scripting.

• Demonstrate Process: Clearly explain the problem you were trying to solve, your approach (including any workflows or prompt tuning), the tools you used, and the outcome or learnings.

• Showcase Technical Skills: Include code samples (e.g., Python scripts for data analysis or AI interaction) if possible, or detailed descriptions of your technical contributions.

• Quantify Results: If you achieved measurable improvements or insights, present them clearly. For an intern role, demonstrating learning and effort is key.

• Prepare to Discuss: Be ready to articulate your thought process, challenges faced, and what you learned from each project.

Challenge Preparation:

• AI Fundamentals: Brush up on core AI/ML concepts, common use cases (like those mentioned in the description), and basic algorithms.

• Python Proficiency: Practice Python for data manipulation (Pandas), scripting, and potentially basic AI/ML libraries (Scikit-learn, TensorFlow/PyTorch basics).

• Problem-Solving: Prepare for hypothetical scenarios related to AI use case prototyping, data analysis, or workflow design. Think about how you would approach defining requirements, testing, and iterating.

• Communication: Practice explaining technical concepts clearly and concisely, as you will need to communicate your ideas and findings effectively.

📝 Enhancement Note: The interview process will likely balance academic qualifications with practical potential. A strong portfolio of academic work or personal projects is crucial for demonstrating initiative and foundational skills for this internship.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python is essential for scripting, data analysis, and AI/ML development. Other scripting languages may also be relevant.

• AI/ML Libraries: Exposure to libraries such as Scikit-learn, TensorFlow, or PyTorch would be beneficial for building and experimenting with AI models.

• Version Control: Git is standard for managing code and collaborating on projects.

• Development Environments: Familiarity with IDEs like VS Code, PyCharm, or Jupyter Notebooks.

Analytics & Reporting:

• Data Analysis Tools: Excel for data manipulation and basic analysis. Potentially libraries like Pandas for more advanced data handling in Python.

• Visualization Tools: PowerPoint for presentations. Libraries like Matplotlib or Seaborn in Python for data visualization.

• System Log Analysis: Familiarity with how to access and interpret system logs.

CRM & Automation:

• Workflow Design: Conceptual understanding of how to design automated workflows for tasks like incident management or service desk operations.

• Scripting: Utilizing scripts to automate routine tasks and data processing.

📝 Enhancement Note: The technology stack focuses on core tools for AI development and data handling. Proficiency in Python and familiarity with common AI/ML libraries are key. The emphasis is on practical application and learning within this stack.

👥 Team Culture & Values

Operations Values:

• Innovation: A drive to explore new technologies like AI and find novel solutions to business problems.

• Collaboration: Working effectively with team members, mentors, and potentially other departments to achieve project goals.

• Learning & Development: A commitment to continuous learning, skill acquisition, and professional growth, especially for interns.

• Efficiency: Striving to improve processes and outcomes through automation and intelligent solutions.

• Data-Driven Approach: Utilizing data analysis to inform decisions, evaluate performance, and refine AI models.

Collaboration Style:

• Cross-functional Integration: Interns will likely work alongside engineers and potentially business analysts, requiring clear communication and understanding of different roles.

• Feedback Exchange: An open culture where feedback is given and received constructively to improve work and learning.

• Knowledge Sharing: Encouragement to share learnings, challenges, and insights within the team, fostering a collective learning environment.

📝 Enhancement Note: Unisys, as a large IT services company, likely fosters a culture that values professional development, teamwork, and practical application of technology. For an intern, demonstrating a proactive learning attitude and collaborative spirit will be highly valued.

⚡ Challenges & Growth Opportunities

Challenges:

• Bridging Academia and Industry: Adapting academic knowledge to real-world enterprise AI challenges and practical prototyping.

• Rapid Learning Curve: Quickly acquiring new skills and understanding complex AI concepts and tools within a limited internship timeframe.

• Prototyping Complexity: Developing functional PoCs that effectively demonstrate AI capabilities for specific business use cases.

• Documentation Standards: Meeting Unisys's standards for technical documentation, ensuring clarity and thoroughness for handover.

Learning & Development Opportunities:

• AI Specialization: Gaining practical experience in specific AI domains like natural language processing (for service desk automation) or predictive analytics (for incident management).

• Professional Skill Development: Enhancing skills in Python, AI libraries, prompt engineering, data analysis, and technical documentation.

• Industry Exposure: Understanding the lifecycle of AI solutions from concept to potential production, within a large IT services organization.

• Mentorship: Receiving guidance from experienced professionals, which can significantly accelerate learning and career development.

📝 Enhancement Note: The challenges presented are typical for an intern role, focusing on the transition from academic learning to practical application. The growth opportunities are substantial, offering a solid foundation for a career in AI and data operations.

💡 Interview Preparation

Strategy Questions:

• "Describe an AI use case you find particularly interesting, and how you might prototype it." (Focus on explaining your thought process, potential tools, and expected outcomes.)

• "How would you approach creating an agentic workflow for automating a repetitive task, like categorizing support tickets?" (Detail your steps for defining tasks, triggers, and response logic.)

• "Imagine you've tuned a prompt for an AI model, and the response quality hasn't improved as expected. What steps would you take next?" (Discuss iterative testing, analyzing model behavior, and alternative tuning strategies.)

• "How do you ensure the documentation for a prototype is clear enough for another engineer to understand and build upon?" (Emphasize structure, detail, and clarity in your explanation.) Company & Culture Questions:

• "What interests you about Unisys and specifically this AI Use Case Prototyping internship?" (Research Unisys's AI initiatives, products, or values. Connect your interests to their work.)

• "How do you collaborate with others on projects, especially when you have different levels of technical expertise?" (Provide examples of teamwork and communication, highlighting your willingness to learn and share.)

• "Describe a time you had to learn a new technical skill quickly. How did you approach it?" (Showcase your learning agility and proactive approach to skill development.) Portfolio Presentation Strategy:

• Select 2-3 Strong Projects: Choose projects that best demonstrate your skills in AI, prototyping, data analysis, or scripting.

• Structure Your Narrative: For each project, clearly state the problem, your role/contribution, the technical approach (tools, methods), the outcome/results, and key learnings.

• Showcase Process, Not Just Results: Explain your thought process, challenges encountered, and how you overcame them. For AI, discuss your approach to prompt tuning or workflow design.

• Be Prepared for Technical Deep Dives: Be ready to discuss specific code snippets, algorithms, or technical choices you made.

• Quantify Impact: If possible, present metrics or measurable improvements achieved, even if it's from academic work.

📝 Enhancement Note: Interview preparation should focus on demonstrating a strong foundational understanding of AI, practical problem-solving skills, and a proactive, collaborative attitude. The portfolio presentation is key to showcasing applied knowledge.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided link on the Unisys careers portal.

• Tailor Your Resume: Highlight relevant coursework, academic projects, and any personal coding or AI exploration that aligns with the internship requirements (AI, prototyping, Python, data analysis). Quantify achievements where possible.

• Prepare Your Portfolio: Gather examples of academic projects, coding assignments, or personal AI experiments. Ensure you can clearly articulate the problem, your solution, the tools used, and the outcome. Be ready to discuss your understanding of agentic workflows and prompt tuning.

• Research Unisys: Understand Unisys's role in the IT services industry, its focus areas, and any publicly available information on its AI initiatives. This will help you tailor your responses and demonstrate genuine interest.

• Practice Interview Responses: Prepare for technical questions related to AI concepts, Python, and data analysis, as well as behavioral questions about teamwork, learning, and problem-solving. Practice explaining your projects concisely.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

---