New opportunity

Data and AI Engineering Intern

Mitsubishi UFJ Financial Group (MUFG) · Singapore

About this role

The Data and AI Engineering Intern will join the Infrastructure Engineering team at MUFG to contribute to the development of an AI-powered Operations Intelligence platform. The goal is to enhance the efficiency of Storage, Backup, Monitoring, and Infrastructure Operations teams. The internship period is from 11 January 2027 to 30 June 2027. Responsibilities include: Project Discovery & Operational Analysis: Collaborating with infrastructure engineers to analyze operational workflows, incident management, and troubleshooting methods to identify AI-driven efficiency opportunities. AI Solution Development: Developing proof-of-concept AI capabilities, including summarizing alerts, explaining infrastructure events, providing troubleshooting guidance, and recommending documentation using Generative AI, RAG, and prompt engineering. Data Integration & Enrichment: Integrating operational data from platforms like Commvault, Dell PowerScale, Dynatrace, and ServiceNow to provide richer operational context. User Experience & Platform Integration: Assisting in exposing AI capabilities through Microsoft Teams and improving user interactions. Requirements: Candidates must be currently pursuing a Bachelor’s Degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Information Systems, Computer Engineering, or related technical disciplines. Proficiency in Python is required, with familiarity in JavaScript or TypeScript, software development concepts, APIs, and version control. Candidates should have an interest in AI/ML and basic knowledge of LLMs, RAG, and vector databases. A basic understanding of IT infrastructure and cloud technologies is expected, while experience with frameworks like LangChain, LlamaIndex, OpenAI, Ollama, or Hugging Face is considered an added advantage.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonJavaScriptTypeScriptAPIsREST servicesVersion controlArtificial IntelligenceMachine LearningGenerative AILarge Language Models (LLMs)Prompt EngineeringRetrieval-Augmented Generation (RAG)Vector DatabasesKnowledge Retrieval SystemsIT infrastructureCloud technologiesMonitoring and observabilityStorageBackup and recoveryIT service managementLangChainLlamaIndexOpenAIOllamaHugging Face

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Operational Workflow Analysis
The intern must actively collaborate with infrastructure engineers to map out complex incident management and troubleshooting workflows. Success depends on translating these technical processes into actionable AI use cases that address real-world pain points in storage, backup, and monitoring.
RAG-based AI Solution Development
A core priority is building proof-of-concept tools using Generative AI and Retrieval-Augmented Generation (RAG). This requires integrating disparate data sources like ServiceNow and Dynatrace to provide engineers with accurate, context-aware troubleshooting guidance.
Platform Integration and UX
The role requires exposing AI capabilities through enterprise interfaces like Microsoft Teams. The intern must ensure that the AI-generated insights are presented in an intuitive, user-friendly manner that enhances, rather than complicates, the daily operations of the infrastructure team.

Likely challenges to think through

Domain Knowledge Gap
The intern must quickly learn the intricacies of enterprise infrastructure platforms like Commvault and Dell PowerScale. To prepare, research how these systems generate alerts and how IT teams typically manage incident lifecycles in large-scale financial environments.
Data Integration Complexity
Integrating data from multiple enterprise systems (ServiceNow, Dynatrace) involves navigating different API structures and data formats. Prepare by reviewing REST API documentation and practicing data normalization techniques to ensure consistent input for LLM-based workflows.
Balancing AI Accuracy with Operational Reliability
In a financial infrastructure context, AI-generated troubleshooting advice must be highly accurate to avoid operational disruption. Prepare by studying RAG best practices, specifically focusing on how to minimize hallucinations and ensure the retrieval system points to verified, high-quality documentation.

Practice your answers

Technical practice

Explain the RAG pipeline for operational data

What to coverDescribe how you would ingest, chunk, and embed operational documentation or incident logs into a vector database. Explain the role of retrieval in providing context to an LLM to answer infrastructure-specific troubleshooting queries.

Python for data integration

What to coverDiscuss how you would use Python to interact with REST APIs from platforms like ServiceNow or Dynatrace. Mention handling JSON responses, authentication, and data transformation for downstream AI processing.

Prompt engineering for infrastructure alerts

What to coverExplain how you would design a prompt to summarize a complex infrastructure alert for an engineer. Discuss techniques like few-shot prompting or chain-of-thought to ensure the output is actionable and accurate.

Vector databases in AI applications

What to coverExplain the purpose of a vector database in a RAG system. Contrast it with traditional relational databases and explain why similarity search is critical for retrieving relevant technical documentation.

Managing AI frameworks

What to coverIf you have used LangChain or LlamaIndex, explain how these frameworks simplify the orchestration of LLM chains. If not, suggest how you would approach learning these to build a proof-of-concept.

Version control in collaborative projects

What to coverDescribe your workflow for using Git in a team environment. Focus on branching strategies and pull requests to ensure code quality when contributing to an AI-powered platform.

Data enrichment strategies

What to coverHow would you correlate data from disparate sources like backup logs (Commvault) and storage metrics (Dell PowerScale)? Explain the importance of a common identifier or timestamp for building a unified operational view.

API fundamentals

What to coverDefine the role of REST services in modern infrastructure monitoring. Explain how you would handle rate limiting or pagination when fetching large datasets from monitoring tools.

Behavioural practice

Collaborating with non-AI stakeholders

What to coverDescribe a time you had to explain a technical concept to someone with a different background. Focus on how you gathered requirements from infrastructure engineers to ensure your AI solution actually solved their operational pain points.

Handling ambiguity in project discovery

What to coverShare an example of a project where the requirements were unclear. Explain how you analyzed the situation, identified the core problem, and proposed a structured approach to move forward.

Prioritizing tasks in a fast-paced environment

What to coverDescribe how you manage your time when balancing development tasks with documentation or analysis. Provide an example of how you decided which feature or use case to prioritize first.

Learning new technologies quickly

What to coverThe role involves experimenting with various AI tools and frameworks. Describe a recent instance where you had to learn a new library or tool independently and how you validated your understanding.

Sharpen your knowledge

Topics to prepare

Retrieval-Augmented Generation (RAG) Architecture
Understand how RAG bridges LLMs with external data sources. Exercise: Design a conceptual workflow where an LLM retrieves specific troubleshooting steps from a documentation database to answer an infrastructure alert. This is directly relevant to the role's goal of building an AI-powered Operations Intelligence platform.
IT Infrastructure Operations Lifecycle
Familiarize yourself with the standard workflows for storage, backup, monitoring, and incident management. Exercise: Research how tools like ServiceNow and Dynatrace generate logs and alerts. This knowledge is essential for identifying where AI can provide meaningful operational context.

Skills to revise

Python for Data Integration
Practice writing scripts to consume data from REST APIs and transform it into structured formats. Exercise: Create a Python script that fetches sample data from a mock API and parses it into a JSON format suitable for an LLM prompt. This aligns with the requirement to integrate operational data from various enterprise platforms.
Prompt Engineering for Technical Context
Develop techniques to guide LLMs in summarizing complex technical logs. Exercise: Take a raw, verbose server error log and craft a prompt that forces the model to extract the root cause and suggest a remediation step in plain language. This is a core responsibility for the AI solution development phase.
Version Control with Git
Ensure proficiency in branching, committing, and merging code. Exercise: Create a small repository, perform multiple feature branches, and practice resolving a merge conflict. This is a standard requirement for collaborative software development within the infrastructure engineering team.

Your preparation checklist

Build a RAG-based prototype
Develop a small project using Python, LangChain, and a vector database to query a local set of technical documentation. This demonstrates your ability to apply RAG concepts to the knowledge retrieval tasks required for the role.
Review IT infrastructure fundamentals
Refresh your knowledge on how monitoring, storage, and backup systems function. Research the basic APIs of tools like ServiceNow or Dynatrace to understand how operational data is typically extracted and integrated.
Prepare a technical project walkthrough
Select a past project involving Python and version control. Be ready to explain your code structure, how you handled API integrations, and how you solved a specific technical challenge during development.
Draft questions on operational workflows
Prepare thoughtful questions for the interviewers, such as: 'What are the most common bottlenecks in current incident management workflows?' or 'How does the team currently prioritize which infrastructure alerts require immediate attention?'
Align with MUFG culture principles
Review the MUFG Culture Principles (Client Centric, People Focused, Listen Up, Speak Up, Innovate & Simplify, Own & Execute). Prepare examples of how you have applied these values in academic or collaborative team settings.

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