AI Solutions Engineer Intern - Bachelor's/Master's Degree
Marvell · Singapore
About this role
Marvell is seeking an AI Solutions Engineer Intern to join their world-class IT team. This role focuses on defining and shaping end-to-end AI projects, from initial concept through prototyping to final demonstration. The intern will work closely with mentors to translate business needs into technical approaches and enhance existing AI frameworks. Responsibilities include developing and validating AI solutions using real-world data, iterating based on feedback, and exploring advanced AI frameworks for use cases such as OCR, image recognition, and multimodal AI. Key focus areas include Generative AI, Agentic AI, LLM-based workflows, prompt engineering, and RAG. Requirements include current enrollment in a Bachelor’s or Master’s degree program in Information Systems, Computer Science, or equivalent fields, with a preference for penultimate year students. Candidates should possess basic knowledge of Python and familiarity with databases or cloud platforms. A strong interest in Generative AI and Agentic AI is required, with prior exposure through coursework, side projects, or LLM APIs considered a plus. Strong analytical, problem-solving, and communication skills are essential. This position may require access to technology subject to U.S. export control laws, and applicants must be eligible to access export-controlled information.
For full details about this role, click “Apply on company site” to view the employer’s original posting.
Skills for this role
PythonDatabricksAWSGenerative AIAgentic AILLMPrompt EngineeringRAGOCRImage RecognitionMultimodal AI
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A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
End-to-End AI Project Lifecycle Management
The role requires taking projects from initial concept through prototyping to final demonstration. You must be prepared to discuss how you structure a project, manage technical milestones, and translate abstract business needs into concrete, actionable technical requirements.
Development of Generative and Agentic AI Solutions
A core focus is building LLM-based workflows, RAG systems, and agentic frameworks. You should be ready to demonstrate your understanding of how these components integrate and how to iterate on them using real-world data and feedback.
Technical Proficiency in AI Frameworks and Cloud Infrastructure
You will be expected to apply Python, Databricks, and AWS to solve complex problems. Prioritize demonstrating your ability to use these tools to build or enhance AI applications, specifically for use cases like OCR, image recognition, or multimodal AI.
Likely challenges to think through
Translating Business Requirements to Technical Solutions
A significant challenge is bridging the gap between business needs and technical implementation. Prepare by practicing how you would interview stakeholders to clarify requirements and how you document your technical approach to ensure alignment with business goals.
Iterative Development Under Feedback
The role emphasizes prototyping and iterating based on feedback. You may face difficulty when initial models or prototypes do not meet performance expectations. Prepare by sharing examples of how you have handled constructive criticism and adjusted your technical approach to improve results.
Navigating Export Control Compliance
The position involves access to technology subject to U.S. export control laws. This adds a layer of administrative and regulatory complexity to the hiring process. Ensure you are prepared to provide accurate information regarding your eligibility to access such information if requested.
03
Practice your answers
Technical practice
Explain the RAG (Retrieval-Augmented Generation) architecture
What to coverDescribe how RAG improves LLM responses by grounding them in external data. Discuss the components involved, such as vector databases, document retrieval, and the integration of retrieved context into the prompt for the model.
Python for Data Processing
What to coverDiscuss your experience using Python libraries for data manipulation or AI workflows. Be prepared to explain how you would clean or prepare real-world data before feeding it into an AI model.
Prompt Engineering Best Practices
What to coverExplain techniques for optimizing prompts to get better performance from LLMs. Mention strategies like few-shot prompting, chain-of-thought, or system instructions to guide model behavior.
Agentic AI vs. Standard LLM Workflows
What to coverDifferentiate between a standard LLM interaction and an Agentic AI workflow. Discuss the concept of agents using tools or making decisions to complete multi-step tasks.
Cloud Platforms and AI Deployment
What to coverIf you have used AWS or other cloud providers, explain how you have used them to host or interact with AI services. If not, discuss how you would approach deploying a prototype to a cloud environment.
Multimodal AI Applications
What to coverDiscuss the challenges and opportunities in handling multimodal inputs like images and text simultaneously. Suggest how you might approach an image recognition or OCR task using modern AI frameworks.
Iterative Prototyping
What to coverExplain your process for validating an AI prototype against real data. Focus on how you incorporate feedback loops to refine model performance or output quality.
Database Familiarity
What to coverDescribe your experience with databases, particularly those used for AI applications like vector databases. Explain why structured or unstructured data storage is critical for AI projects.
Behavioural practice
Translating Business Needs to Technical Solutions
What to coverProvide an example where you had to understand a non-technical requirement and translate it into a technical project. Focus on how you clarified the goal and selected the right approach.
Collaborating in a Team Environment
What to coverDescribe a situation where you worked with others to complete a project. Highlight your communication style and how you contributed to the team's success.
Handling Technical Challenges
What to coverShare an example of a time you encountered a difficult problem during a project. Explain the steps you took to troubleshoot the issue and what you learned from the experience.
Eagerness to Learn New Technologies
What to coverDescribe a time you had to quickly learn a new AI framework or tool to complete a task. Explain your learning process and how you applied the new knowledge effectively.
02
Sharpen your knowledge
Topics to prepare
Retrieval-Augmented Generation (RAG) Architecture
Understand the conceptual flow of RAG, including document ingestion, vector database storage, and retrieval mechanisms. This is critical as the role involves building LLM-based workflows to solve business problems using real-world data.
Agentic AI Workflows
Study the difference between standard LLM prompting and agentic systems where models use tools to perform multi-step tasks. This aligns with the job's focus on developing Agentic AI solutions.
Skills to revise
Python for Data Processing
Practice writing clean, modular Python code to manipulate datasets, as this is the primary language for developing and validating AI solutions at Marvell.
Prompt Engineering Techniques
Exercise iterative prompt refinement using techniques like Chain-of-Thought or Few-Shot prompting to improve model output accuracy. This is a core responsibility for enhancing existing AI frameworks.
Cloud and Database Familiarity
Review basic concepts of cloud platforms (like AWS) and database interactions (SQL or NoSQL). The role requires familiarity with these environments to support end-to-end AI project prototyping.
04
Your preparation checklist
Build a RAG-based prototype
Develop a small-scale project using Python that implements Retrieval-Augmented Generation (RAG). Focus on connecting a local document store to an LLM API to demonstrate your ability to handle data-driven AI workflows.
Review core AI frameworks and cloud basics
Refresh your knowledge of Python libraries relevant to AI and familiarize yourself with the basic concepts of AWS or Databricks. Be prepared to discuss how these platforms support end-to-end AI project lifecycles.
Prepare a structured project walkthrough
Select one academic or personal project involving AI or data analysis. Prepare to explain the business problem, your technical approach, the data used, and how you iterated on the solution based on testing or feedback.
Practice explaining Agentic AI concepts
Study the fundamentals of Agentic AI and LLM-based workflows. Be ready to articulate how autonomous agents differ from standard prompt engineering and provide examples of potential use cases in an enterprise IT environment.
Formulate questions about the IT team's AI integration
Prepare thoughtful questions for the interviewers regarding how the IT team balances innovation with existing infrastructure, such as: 'How does the team prioritize which AI use cases to prototype?' or 'What are the primary challenges in deploying RAG solutions within your current data architecture?'
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