Structure your answer around a specific project, clearly separating the RAG pipeline, AI agent design, and tool orchestration layers. Emphasize the trade-offs you made and the outcomes achieved, tying them to the role's requirements.
Pro tip: Quantify the impact of your architecture choices (e.g., latency reduction, accuracy improvement) and be ready to discuss a failure or lesson learned—this shows maturity and depth.
Briefly describe the project's goal, your role, and the scale (e.g., users, data volume). This grounds your answer and shows relevance.
Detail your retrieval and generation components: embedding model, vector database, chunking strategy, and how you handled hallucinations or relevance.
Outline the agent's architecture: planning, memory, and decision-making. Mention frameworks used (e.g., LangChain, AutoGen) and how you ensured reliability.
Explain how the agent invokes external tools/APIs, manages state, and handles errors. Highlight any orchestration patterns (e.g., ReAct, function calling).
Discuss key decisions (e.g., latency vs. accuracy, cost vs. performance) and their impact. End with measurable results and lessons learned.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is actually a question the poster is asking the community, not something asked by the interviewer.
Start by acknowledging that you've researched the typical interview process for software engineering roles at Lilly, then outline the core DSA topics and difficulty levels you expect. Emphasize that you're prepared for a mix of easy to medium problems, with a focus on practical problem-solving and clean code.
Pro tip: Mention that you've practiced explaining your thought process aloud and writing production-quality code, as interviewers at Lilly value communication and maintainability alongside correctness.
State that you've looked into the interview process and understand that Lilly's technical rounds typically cover fundamental DSA topics.
Enumerate the key DSA areas such as arrays, strings, linked lists, trees, graphs, sorting, searching, and dynamic programming.
Explain that problems usually range from easy to medium difficulty, with occasional hard problems depending on the role's seniority.
Describe how you've prepared by solving problems on platforms like LeetCode and practicing mock interviews.
Relate the DSA topics to real-world software engineering tasks at Lilly, showing how these skills apply to the job.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.