Applied AI and LLM Integration
As an AI Engineer, your ability to leverage modern AI paradigms is critical. This area evaluates your practical experience with Large Language Models, prompt engineering, and Retrieval-Augmented Generation (RAG). Strong performance means demonstrating a nuanced understanding of how to constrain model hallucinations, optimize latency, and handle context windows effectively.
Be ready to go over:
- RAG Architectures – Understanding vector databases, embedding models, and retrieval strategies.
- Prompt Engineering & Fine-Tuning – Knowing when to rely on zero-shot prompting versus when to fine-tune a model using LoRA or QLoRA.
- Model Evaluation – Techniques for evaluating generative AI outputs systematically.
- Advanced concepts (less common) – Agentic workflows, multi-modal model integration, and custom decoding strategies.
Example questions or scenarios:
- "Walk me through how you would design a RAG system to query thousands of dense legal documents for a consulting engagement."
- "How do you handle situation where an LLM confidently hallucinates an answer in a client-facing application?"
- "Explain the trade-offs between using a managed LLM API (like OpenAI) versus hosting an open-source model (like Llama 3) internally."
AI Infrastructure and MLOps
For the AI Lab Infrastructure side of the role, this is the most critical evaluation area. We need to know that you can build the pipes that keep our models running securely and efficiently. Interviewers are looking for candidates who treat ML models as software that needs rigorous testing, deployment, and monitoring.
Be ready to go over:
- Model Deployment – Containerizing models with Docker and orchestrating them via Kubernetes or cloud-native services.
- CI/CD for Machine Learning – Automating model training, testing, and deployment pipelines.
- Monitoring & Observability – Tracking data drift, concept drift, and model performance degradation in production.
- Advanced concepts (less common) – Distributed training architectures, GPU memory optimization, and custom CUDA kernels.
Example questions or scenarios:
- "How would you design an infrastructure to serve a highly requested ML model with strict latency requirements?"
- "Describe your approach to setting up a CI/CD pipeline for a machine learning project."
- "What metrics would you monitor for an NLP model deployed in production, and how would you detect drift?"
Software Engineering & System Design
AI engineers are, fundamentally, software engineers. This area tests your ability to write clean, maintainable, and scalable code. Strong candidates will show proficiency in Python, an understanding of software design patterns, and the ability to design distributed systems that integrate AI seamlessly into broader product ecosystems.
Be ready to go over:
- API Design – Building robust RESTful or GraphQL APIs using frameworks like FastAPI or Flask.
- Database Design – Structuring relational (PostgreSQL) and non-relational (MongoDB, Redis) databases.
- Scalability & Reliability – Designing systems that can handle concurrent users, large data volumes, and failovers gracefully.
- Advanced concepts (less common) – Event-driven architectures, stream processing (Kafka), and microservices orchestration.
Example questions or scenarios:
- "Design a system architecture for an internal tool that allows consultants to upload massive datasets and run predictive models asynchronously."
- "Write a Python function to process and clean a streaming dataset before it hits our inference endpoint."
- "How do you ensure data security and compliance when designing systems that handle sensitive client information?"
Behavioral and Stakeholder Management
At Berkeley Research Group, technical brilliance must be paired with consulting skills. This area evaluates how you handle conflict, influence decisions without authority, and manage the expectations of non-technical stakeholders. A strong performance involves using the STAR method to tell compelling stories about your past experiences.
Be ready to go over:
- Navigating Ambiguity – How you proceed when requirements are vague or constantly shifting.
- Cross-Functional Collaboration – Working with domain experts, product managers, and external clients.
- Failing Forward – Discussing a time a project failed and what you learned from it.
- Advanced concepts (less common) – Leading technical strategy shifts or mentoring junior engineers.
Example questions or scenarios:
- "Tell me about a time you had to explain a complex machine learning limitation to a non-technical stakeholder."
- "Describe a situation where you had to push back on a feature request because it wasn't technically feasible or scalable."
- "How do you prioritize your engineering tasks when multiple teams are depending on your AI infrastructure?"
