New opportunity

AI Forward Deployed Engineering (AI FDE) Intern

Univers · Singapore

About this role

As an AI Forward Deployed Engineering (AI FDE) Intern at Univers, you will collaborate with AI scientists, engineers, and product teams to develop AI solutions for real-world operational challenges. The role focuses on translating ambiguous operational problems into clear AI use cases and building practical, impactful solutions. You will contribute to the entire AI solution lifecycle, from problem identification and rapid prototyping to model evaluation and production support. Responsibilities: Collaborate with cross-functional teams to develop AI prototypes and proof-of-concepts. Apply modern AI technologies including LLMs, computer vision, multimodal AI, and AI agents. Build intelligent workflows and agentic systems using enterprise and operational data. Assist in evaluating and integrating foundation models for domain-specific applications. Translate operational challenges into technical solutions. Contribute to AI experiments, benchmarking, and technical documentation. Qualifications: Currently pursuing a Bachelor's (senior), Master's, or Ph.D. degree in Computer Science, Engineering, AI, Data Science, Electrical Engineering, or related fields. Strong programming skills in Python are required; experience with PyTorch or similar frameworks is preferred. A technical background in machine learning, LLMs, computer vision, or agentic systems is expected. Strong analytical, problem-solving, and communication skills are essential. Experience through coursework, research, personal projects, or previous internships is considered a plus. What You'll Gain: Hands-on experience with foundation models and modern AI engineering. Exposure to the full AI development lifecycle in a professional environment. Mentorship from experienced AI scientists and product leaders. Opportunity to work on meaningful projects addressing real-world operational challenges.

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Skills for this role

PythonPyTorchMachine LearningLarge Language ModelsComputer VisionMultimodal AIAgentic SystemsData ScienceSoftware Engineering

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

Translating Ambiguous Operational Problems into AI Use Cases
The core of the Forward Deployed Engineering role is bridging the gap between complex, real-world operational challenges and technical AI solutions. You must demonstrate the ability to listen to domain experts, identify where AI can provide measurable impact, and define clear, actionable technical requirements from vague problem statements.
Rapid Prototyping and Full-Lifecycle Development
You will be expected to contribute across the entire AI lifecycle, from initial experimentation and benchmarking to building functional prototypes and supporting production-ready systems. Proficiency in Python and familiarity with frameworks like PyTorch are essential to move quickly from concept to a working proof-of-concept.
Cross-Functional Collaboration and Communication
Success in this role requires working effectively with AI scientists, product managers, and GTM teams. You must be able to document your findings, present technical concepts clearly to non-technical stakeholders, and integrate feedback from diverse team members to refine your AI models and workflows.

Likely challenges to think through

Managing Technical Complexity in Real-World Environments
Applying foundation models to domain-specific operational data often involves noisy, unstructured, or large-scale datasets. You may face difficulty in model evaluation and adaptation. Prepare by reviewing techniques for fine-tuning, RAG (Retrieval-Augmented Generation), or agentic workflows that handle real-world data constraints.
Balancing Research Agility with Production Standards
The role requires rapid prototyping, but the goal is to deliver production-ready impact. The challenge lies in maintaining code quality and documentation standards while iterating quickly. Prepare by practicing modular coding, version control, and writing clear technical documentation for your past projects.

Practice your answers

Technical practice

Translating Ambiguous Operational Problems into AI Use Cases

What to coverExplain your process for breaking down a vague business requirement into a technical specification. Focus on identifying key data inputs, defining success metrics, and determining which AI modality (e.g., LLM, computer vision) is most appropriate for the task.

Evaluating Foundation Models for Domain-Specific Tasks

What to coverDescribe the criteria you use to evaluate a foundation model's performance for a specific domain. Discuss trade-offs between zero-shot performance, fine-tuning, and RAG (Retrieval-Augmented Generation) when adapting models to proprietary operational data.

Designing and Implementing Agentic Workflows

What to coverDiscuss the architecture of an AI agentic system. Explain how you manage state, tool selection, and error handling when an agent is tasked with executing multi-step workflows using external APIs or enterprise data.

Rapid Prototyping with PyTorch

What to coverDescribe your workflow for moving from a research idea to a functional prototype in PyTorch. Highlight how you manage data pipelines, model training, and iterative testing to ensure the prototype is viable for further development.

Handling Multimodal Data in AI Systems

What to coverHow do you approach integrating disparate data sources like sensor telemetry, text logs, and visual data into a unified AI model? Discuss the challenges of data alignment and feature extraction in a multimodal context.

Benchmarking and Model Evaluation

What to coverExplain your methodology for benchmarking AI models against real-world operational data. Address how you handle data drift, evaluate edge cases, and ensure the model meets performance requirements before production deployment.

Software Engineering Best Practices for AI

What to coverHow do you ensure your AI code is maintainable, scalable, and well-documented? Discuss the importance of version control, modular code design, and creating clear technical documentation for cross-functional team collaboration.

Deployment and Production Support

What to coverWhat are the primary challenges when transitioning an AI prototype to a production-ready system? Focus on considerations like latency, resource constraints, and the necessity of monitoring model performance in real-time operational environments.

Behavioural practice

Collaborating in Multidisciplinary Teams

What to coverDescribe a situation where you had to explain a complex technical concept to a non-technical stakeholder or domain expert. How did you ensure alignment and move the project forward effectively?

Navigating Ambiguity in Projects

What to coverProvide an example of a project where the requirements were unclear or changed mid-way. How did you define the scope, manage expectations, and maintain progress toward a deliverable?

Learning and Adapting to New Technologies

What to coverThe AI field evolves rapidly; describe a time you had to quickly learn and apply a new framework or tool to solve a specific problem. What was your learning strategy and how did you validate your understanding?

Problem-Solving Under Constraints

What to coverTell me about a time you faced a significant technical roadblock during a project. How did you troubleshoot the issue, and what steps did you take to find a viable solution within the project's constraints?

Sharpen your knowledge

Topics to prepare

AI Solution Lifecycle
Understand the progression from problem identification and ambiguity resolution to prototyping, evaluation, and production support. Exercise: Map a hypothetical operational challenge (e.g., optimizing sensor data for energy efficiency) to a structured AI workflow, defining success metrics and evaluation criteria.
Agentic Systems and Intelligent Workflows
Study the architecture of AI agents that interact with operational data to perform tasks. Exercise: Review documentation on agent frameworks to understand how to design a system that can autonomously handle a multi-step workflow using external tools or APIs.
Foundation Model Integration
Learn the process of adapting and integrating pre-trained models for domain-specific applications. Exercise: Research techniques for fine-tuning or RAG (Retrieval-Augmented Generation) to tailor a foundation model to a specific dataset, focusing on benchmarking performance.

Skills to revise

Python for AI Engineering
Demonstrate proficiency in writing clean, modular code for data processing and model interaction. Exercise: Build a script that processes a sample operational dataset and performs a basic transformation or analysis task relevant to energy or sensor data.
PyTorch Framework Proficiency
Apply PyTorch to build or modify neural network architectures. Exercise: Implement a simple model in PyTorch that performs a classification or regression task on a public dataset, ensuring you can explain the training loop and evaluation steps.
Technical Communication for Cross-Functional Teams
Practice translating complex technical concepts into clear documentation or presentations for non-technical stakeholders. Exercise: Draft a one-page technical summary of a past project, focusing on the problem, the AI approach taken, and the measurable impact achieved.

Your preparation checklist

Prepare a technical project walkthrough
Select a project where you applied machine learning or AI to a real-world problem. Be ready to explain your choice of model, how you handled the data, and how you evaluated the solution's effectiveness, specifically highlighting any experience with Python and PyTorch.
Research the intersection of AI and energy systems
Study how AI agents and foundation models are currently used in industrial or operational settings, such as decarbonization or energy management. This demonstrates your ability to translate ambiguous operational challenges into technical AI use cases.
Review your experience with agentic systems
Prepare to discuss your familiarity with building intelligent workflows or agentic systems. If you have experience with LLMs or multimodal AI, be ready to describe how you integrated these technologies to solve a specific task.
Develop questions about the AI lifecycle at Univers
Prepare thoughtful questions for the interviewers regarding how they move from rapid prototyping to production-ready systems, and how they balance research with practical, measurable operational impact.
Practice explaining technical concepts to non-technical stakeholders
Since the role involves collaborating with product and GTM teams, practice summarizing a complex technical challenge you solved and explaining its business value in clear, concise language.

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