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Hi Venkat, I’ve worked as a TAM and coached candidates for AWS Sr. TAM interviews. The phone screen typically evaluates AWS technical depth, customer management, leadership, and real-world scenario handling. I’d suggest these topics: 1. AWS technical fundamentals — architecture, networking, security/IAM, reliability, databases, monitoring, cost optimization. 2. Customer-facing scenarios — escalations, difficult customers, competing priorities, executive communication. 3. Technical troubleshooting — how you approach outages, performance issues, and ambiguous problems. 4. Leadership & influence — influencing without authority, stakeholder management, driving adoption. 5. Cloud transformation — migration, modernization, operational excellence, Well-Architected thinking. I offer 1:1 coaching with role-specific preparation + mock interview and feedback. Happy to help you prepare specifically for your upcoming screen

Post a certain length of experience, the walls b/w pure technical and product roles become thin. Some directors at successful product org are those who have been engineering leads all through their career. Basically, while delivering a successful product as Tech-lead, you will gradually know how customer pain points are captured, converted to requirements - features, scoped, planned and delivered. I see this transition very natural for you. I think you should a) brush up on core PM deliverables viz. PRFAQs/Proposals, BRDs/PRDs, Vision and Scoping documentation, etc., b) deep-dive into instruments of customer research and feedback collation viz. surveys (primary and secondary), focus groups, POC data collection, etc., and c) focus on process to establish short/long term vision at product/portfolio level from a pure product management perspective. You will be tested on these skills for sure during PM interviews. I am not too concerned of your technical capabilities. However brush up on trade-offs PMs make on product features and choice of technology. Feel free to let me know if you have any follow-up questions.

I would evaluate a RAG system at three levels. First is retrieval quality. I would measure Recall@K to determine whether the system retrieved the necessary evidence, Precision@K to measure how much of the retrieved context was relevant, and MRR or nDCG to evaluate ranking quality. Second is answer quality. Important metrics include answer correctness, relevance, completeness, groundedness, citation accuracy, and hallucination rate. I would also measure unanswerable-question detection, because a reliable system must refuse to answer when the available evidence is insufficient. Third is production reliability. My non-functional metrics would include p50, p95, and p99 latency; time to first token (TFT); throughput; availability; timeout and error rates; cost per successful query; index freshness; ingestion success rate; and mean time to recovery. For security-sensitive systems, I would also measure unauthorized-document retrieval, prompt-injection success, and sensitive-data leakage, with a target of zero. For example, initial targets might be Recall@10 above 90%, groundedness and citation accuracy above 95%, hallucination below 2%, p95 latency below three seconds, and availability above 99.9%. I would create a representative evaluation dataset containing answerable, unanswerable, ambiguous, adversarial, and permission-sensitive questions. I would run it as an offline regression suite before each release and combine it with production monitoring and sampled human reviews. Separating retrieval, generation, and infrastructure metrics allows us to identify exactly where a RAG failure occurred.

L4 engineers build out solutions for well-defined problems with code output focus. That's why L4 candidates are not assessed on system designs. For growth opportunities, L4 engineers already working at Google could slowly take on system design opportunities to prepare for an L5 promotion. The roles and expectations of engineers at each level are clearly defined in writing at Google.

1) “Tell me about a time you used data or experimentation to drive a decision in a high-ambiguity environment.” • Skills assessed: Analytical thinking, comfort with ambiguity, ability to design experiments, data-driven decision-making. • What to highlight: How you framed the problem, the data or signals you gathered, the hypotheses you tested, and the tradeoffs you considered. End with the outcome and what you learned—even if the result wasn’t perfect. 2) “How would you explain a complex AI system to a non-technical stakeholder and get buy-in?” • Skills assessed: Communication, influence without authority, user empathy, ability to simplify complexity. • What to highlight: How you broke down technical concepts into simple analogies, how you tailored the message to the stakeholder’s goals, and how you balanced transparency about risks/limitations with a clear articulation of value. 3) “Imagine the model you launched is underperforming—how do you triage and prioritize next steps?” • Skills assessed: Problem-solving, prioritization, cross-functional collaboration, resilience. • What to highlight: Your structured approach—diagnosis (data quality, model accuracy, user adoption), prioritization of fixes, how you’d partner with engineering/data science, and how you’d communicate updates to stakeholders. Emphasize learning fast and iterating.

E5s are Meta's workhorses. Therefore, they have to be very productive and own the subsystem technical direction. They need to lead E4s and E3s along with collaborating with E6s. They need to be driven to action, work on their features and support other engineers when SEVs (site events) occur. They're also expected to participate in org wide initiatives and help streamline the developer experience and operational excellence initiatives