Prepare for senior AI engineering roles through hands-on practice, project deep dives, and mock interviews. Experienced engineers should demonstrate that they can design, build, evaluate, and operate AI systems—and defend their decisions.
EY is a professional services firm; JPMorgan Chase and Wells Fargo are banks. Tailor your practice to consulting delivery and enterprise banking scenarios.
EY’s recent senior postings emphasize Python/API engineering, cloud deployment, RAG, agents, governance, and stakeholder communication. One AI/ML Senior Consultant posting specifically targets 6–8 years of experience. This supports making engineering depth and delivery ownership central to your preparation; it doesn’t establish the exact interview format. [1, 2]
Preparation framework
| Area | Practice topics | Evidence of a strong answer |
|---|---|---|
| Python, SQL, backend | Coding problems, async/concurrency, APIs, database queries, testing | Working code, edge cases, complexity, clear reasoning |
| ML and LLM fundamentals | Leakage, validation, embeddings, transformers, fine-tuning | Explain mechanisms and choose appropriate approaches |
| RAG and agents | Retrieval, chunking, reranking, tool use, state, failure handling | Diagnose failures and justify architecture choices |
| AI evaluation | Retrieval quality, groundedness, task success, evaluation datasets | Define measurable acceptance criteria |
| Production engineering | Deployment, monitoring, latency, cost, versioning, rollback | Explain how the system behaves under load and failure |
| Security and governance | Access control, sensitive data, prompt injection, auditability | Identify risks and implement concrete controls |
| Ownership and communication | Project walkthroughs, tradeoffs, incidents, leadership | Separate your contribution from the team’s work |
Use a four-week preparation plan, adjusted to your interview date and current strengths:
| Week | Focus | Outcome |
|---|---|---|
| 1 | Baseline assessment, Python/SQL, project narratives | Identify gaps and prepare two convincing project walkthroughs |
| 2 | LLM fundamentals, RAG, agents, evaluation | Answer technical questions through mechanisms and examples |
| 3 | System design, deployment, security, reliability | Design an enterprise AI application and defend its tradeoffs |
| 4 | Timed coding and full mock interviews | Improve weak areas and make answers concise under pressure |
Answer each practice question before checking the notes. Assess correctness, depth, tradeoffs, production awareness, and clarity, identify gaps, and practise a follow-up. Use examples from your real experience wherever possible.
Whenever you mention a technology, explain why you chose it, what alternative you considered, and how you measured whether it worked. For example, “We used a vector database” needs a discussion of retrieval requirements, filtering, scale, and measured quality.
Project deep dive
Walk me through the most substantial GenAI application you worked on, from business problem to deployment. Explain the architecture, your personal contribution, one important technical tradeoff, how you evaluated quality, and the hardest failure you encountered.
Aim for a 3–5 minute interview answer, roughly 400–600 words. If it was a prototype, describe its actual stage accurately.
Before practising, note your interview timeline, target location, and main cloud/framework stack. Use these details to select relevant project examples and interview scenarios.