Technical practice
Designing Agentic Workflows with LangChain
What to coverExplain how you would architect a robust AI agent runtime using LangChain. Discuss how you handle state management, tool selection, and error recovery when building agentic workflows for enterprise applications.
Implementing RAG for Enterprise Data
What to coverDescribe your approach to building a Retrieval-Augmented Generation (RAG) pipeline. Focus on how you ensure data privacy, manage context window limitations, and optimize retrieval accuracy using techniques like semantic search or knowledge graphs.
Microservices and Event-Driven Architecture
What to coverHow do you design scalable microservices for AI platforms? Discuss your experience with event-driven patterns, specifically using Kafka, and how you ensure consistency and performance across distributed systems.
Observability in AI Systems
What to coverGiven the non-deterministic nature of LLMs, how do you implement observability? Explain how you would use tools like Dynatrace or custom logging to monitor agent performance, latency, and hallucination rates in production.
Cloud-Native Delivery and Kubernetes
What to coverDescribe your process for deploying AI services in a Kubernetes environment. Address how you manage CI/CD pipelines, container orchestration, and resource scaling for compute-intensive machine learning workloads.
API Design and Integration Patterns
What to coverWhat are the key considerations when designing APIs for an AI platform that must integrate with existing ERP systems? Discuss security, versioning, and how you handle synchronous vs. asynchronous communication patterns.
Evaluating Agent Performance
What to coverHow do you define and measure the success of an AI agent? Explain your methodology for creating evaluation frameworks that test for both functional correctness and business-level performance metrics.
Technical Trade-offs in Architecture
What to coverDescribe a time you had to make a significant architectural trade-off between performance, maintainability, and time-to-market. How did you present this decision to stakeholders and ensure alignment?
Behavioural practice
Mentoring and Team Culture
What to coverDescribe a situation where you mentored a junior engineer to improve their technical output or professional growth. What specific actions did you take, and what was the impact on the team's performance?
Navigating Agile Challenges
What to coverShare an example of how you handled a situation where project requirements changed significantly mid-sprint. How did you maintain team morale and ensure the delivery of high-quality code under pressure?
Cross-Functional Collaboration
What to coverProvide an example of how you collaborated with non-technical stakeholders, such as product owners or data scientists, to translate complex business needs into a technical roadmap.
Handling Technical Disagreements
What to coverTell me about a time you disagreed with a technical decision made by a peer or leadership. How did you communicate your concerns, and how was the final resolution reached?