New opportunity

Internship - Artificial Intelligence, Automation, and Database

Infineon · Singapore

About this role

As an Intern, you will support the development of AI-driven automation, data engineering, and analytics solutions to improve engineering productivity and operational efficiency. You will work with technologies such as Python, SQL, Tableau, machine learning, and AI tools to automate workflows, develop data visualizations, build AI models and agents, and manage data across multiple platforms. The role offers exposure to real-world engineering systems, cross-functional collaboration, and the application of artificial intelligence, digitalization, and data analytics in a global technical environment. Key responsibilities include pathfinding automation of engineering data using shell and python scripts, enhancing coding development with AI-assisted tools, developing user interfaces and databases for microcontroller teams, and setting up automation schedulers in UNIX. You will also build or utilize supervised machine learning models, create interactive data visualizations, and develop AI agents for proactive notifications. Qualifications include a Bachelor's degree in Computer Science, Electrical/Electronics/Computer Engineering, Information Technology, Business Analytics, or Data Science. Mandatory proficiency in hands-on Python or similar scripting languages is required, with a focus on manual coding. Preferred qualifications include prior internship or work experience in Python scripting for data processing and AI, and knowledge of databases like SQL, Django, or Oracle. The preferred internship period is from January 27 to June 27.

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

Skills for this role

PythonSQLTableauMachine LearningData MiningShell ScriptingJenkinsGitlabJAMAJIRAUNIXC-shellOracleSAPKLUSA APIWebexMicrosoft TeamsPerlJavascriptTk/TCLDjango

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

Hands-on Python Scripting and Automation
The role requires over 80% of time spent on coding, specifically developing automation scripts from scratch without AI assistance. You must demonstrate proficiency in Python or similar languages to build workflows, manage data, and interface with internal databases.
Database Management and Data Structuring
You are expected to set up and maintain data structures and datasets for automation. This involves interfacing with commercial and internal databases like SQL, Oracle, and SAP using Python APIs to ensure data is accessible for analytics and AI models.
AI Model Development and Integration
You will build or utilize supervised machine learning models and AI agents to improve operational efficiency. This includes mining internal data sources like incident reports and regression logs to create proactive notification systems.
Technical Tooling and Visualization
You must develop and maintain interactive data visualizations using Tableau and create user interfaces for microcontroller development teams. Familiarity with the broader engineering ecosystem, including Jenkins, JIRA, and Gitlab, is essential for pathfinding automation solutions.

Likely challenges to think through

Balancing Manual Coding with AI-Assisted Development
While you are expected to use AI tools like Gitlab Co-pilot to speed up development, the core requirement is the ability to hand-code from scratch without AI assistance. Prepare by practicing fundamental Python logic and debugging without relying on LLM-generated code.
Navigating Complex Legacy and Proprietary Systems
You will interact with a variety of internal databases and proprietary APIs (e.g., KLUSA, SAP). The challenge lies in understanding how to bridge these disparate systems through automation. Prepare by reviewing how to write robust API integration scripts and handling data mapping between different formats.
Operating in a Global Hybrid Environment
The role requires working with colleagues across Asia Pacific and Europe with minimal supervision. You must be self-driven to learn and communicate effectively in a remote or hybrid setting. Prepare by highlighting examples of independent project management and proactive cross-functional communication.

Practice your answers

Technical practice

Python Scripting for Data Automation

What to coverExplain how you would use Python to automate the extraction and processing of data from a structured source like a SQL database. Discuss your approach to error handling and ensuring data integrity during the automation process.

Supervised Machine Learning Workflow

What to coverDescribe the steps involved in building a supervised machine learning model, from data preprocessing to evaluation. Mention how you would select appropriate features and metrics to measure the model's performance in an engineering context.

Database Interfacing and Management

What to coverHow do you approach connecting a Python script to a database like Oracle or SQL? Discuss the importance of using secure connection methods and how you would structure data for efficient retrieval.

UNIX Automation and Scheduling

What to coverExplain how you would use cronjobs or shell scripts to automate recurring tasks in a UNIX environment. Describe how you would monitor these tasks to ensure they complete successfully.

Data Visualization with Tableau

What to coverWhen creating a dashboard in Tableau, how do you determine which visualizations best represent specific engineering data sets? Explain your process for cleaning raw data before importing it into a visualization tool.

AI Agents for Proactive Notifications

What to coverDescribe the architecture of a simple AI agent designed to send automated alerts via messaging platforms like Webex or Microsoft Teams. Focus on how you would trigger these notifications based on specific data anomalies.

Version Control and Collaboration

What to coverWhy is version control essential when working on automation scripts in a team environment? Explain how you would use Gitlab to manage code changes and resolve potential conflicts.

Data Mining from Engineering Reports

What to coverIf tasked with mining data from unstructured sources like incident reports or regression logs, what techniques would you use to extract meaningful indicators? Suggest how you would validate the accuracy of the extracted information.

Behavioural practice

Learning New Technical Tools

What to coverDescribe a situation where you had to learn a new programming language or software tool quickly to complete a project. Explain the steps you took to master the tool and how you applied it to your work.

Working with Minimal Supervision

What to coverProvide an example of a project where you were given significant autonomy. How did you manage your time, set milestones, and ensure you were meeting the project's objectives without constant guidance?

Cross-Functional Collaboration

What to coverTell me about a time you worked with a team from a different background or department. How did you ensure clear communication and alignment on technical goals?

Problem-Solving Under Pressure

What to coverDescribe a time when an automated process you built failed or produced unexpected results. How did you troubleshoot the issue, and what steps did you take to prevent it from happening again?

Sharpen your knowledge

Topics to prepare

Supervised Machine Learning Fundamentals
The role requires building or utilizing supervised models to improve efficiency. Review the end-to-end lifecycle of supervised learning, including data preprocessing, feature engineering, model selection, and evaluation metrics. Exercise: Select a dataset and build a predictive model from scratch using Python libraries like Scikit-Learn, focusing on manual implementation rather than relying on automated AI code generation.
Database Architecture and Data Interfacing
You will manage data across multiple platforms and interface with SQL, Oracle, and SAP. Review relational database concepts, normalization, and how to interact with databases using Python APIs. Exercise: Write a Python script that connects to a local SQL database, performs a complex query to join multiple tables, and processes the result set for further analysis.
UNIX Automation and Scheduling
The position involves setting up automation schedulers in a UNIX environment. Review cronjobs, shell scripting, and load-sharing file systems. Exercise: Create a C-shell or Bash script that automates a routine task, such as file cleanup or data backup, and configure it to run at a specific time using a cronjob.

Skills to revise

Hands-on Python Scripting
The role requires >80% coding time, specifically manual coding without AI assistance. Focus on writing clean, efficient, and modular Python code. Exercise: Implement a data processing pipeline that reads raw CSV files, cleans the data, performs statistical analysis, and exports the results, ensuring you write the logic manually without using AI-assisted coding tools.
Interactive Data Visualization with Tableau
You will be responsible for creating and maintaining interactive dashboards. Review how to connect Tableau to various data sources and design effective visualizations. Exercise: Take a raw dataset, structure it appropriately, and build a Tableau dashboard that includes interactive filters and calculated fields to highlight key performance indicators.

Your preparation checklist

Demonstrate manual Python proficiency
Prepare a portfolio of scripts written from scratch without AI assistance. Focus on data processing, automation, and file manipulation tasks to prove you can meet the requirement of coding over 80% of the time without reliance on generative tools.
Build a data-driven automation project
Create a project that integrates a database (SQL or similar) with a Python script to automate a repetitive task. Include a visualization component using Tableau to show how you transform raw data into actionable insights for stakeholders.
Prepare a machine learning model walkthrough
Be ready to explain a supervised machine learning project you have completed. Focus on the data cleaning, feature selection, and model evaluation phases, and be prepared to discuss how you would apply such models to improve operational efficiency.
Review UNIX and shell scripting fundamentals
Refresh your knowledge of UNIX environments, specifically C-shell scripting and cronjobs. Since the role involves setting up automation schedulers, be prepared to discuss how you manage tasks in a command-line environment.
Formulate questions about engineering workflows
Prepare thoughtful questions for the interviewer regarding the team's current automation challenges, such as: 'How does the team currently balance manual data processing with AI-driven automation?' or 'What are the most common bottlenecks in the microcontroller development data pipeline?'

Availability has not yet been verified. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in Singapore. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…