Hi guys, I have an upcoming SDE 2 interview loop with the **Amazon Fulfillment Technologies (AFT Flow)** team in Bangalore.
Does anyone have recent experience interviewing with this specific team? I want to know what exact algorithmic patterns they ask in the coding rounds and what kind of LLD/HLD scenarios they expect for a fulfillment/logistics platform.
Thanks!
I have 13 years of work ex and working as lead software engineer, looking for a prep buddy for sde3 roles.please DM
Just finished my Amazon SDE Intern interviews (2 rounds) and honestly not sure how to feel about my chances.
Background:
- ~1500+ DSA problems solved
- LC peak around 1900
- Strong focus on backend + GenAI projects
Round 1:
Started with introduction and then interviewer went deep into my GenAI project (RAG, validation, why not just use ChatGPT directly, handling generic outputs, etc.).
Then 2 DSA questions:
Gas station type greedy problem
Boats to save people (2 pointers/greedy)
He grilled me on optimizations and simplifications but overall discussion was collaborative. Then one LP question around conflict in a team.
Round 2:
Again started with intro + project discussion. Interviewer asked about how I debug AI outputs and validate Gemini responses.
Then 2 DSA questions:
We had prices array + M coupons. Each coupon could reduce a value by dividing by powers of 2. Needed to minimize total cost.
I first explored some non-optimal ideas, then derived greedy + max heap approach (always reduce current maximum). Interviewer heavily grilled complexity and reasoning.
DP-style question where if we take value x, we cannot take adjacent values (x-1 and x+1).
I derived the frequency/value accumulation transformation myself and explained recursion + memoization/DP approach. Interviewer asked me to manually explain the recurrence on a testcase instead of directly writing DP.
Overall both interviews were pretty probing. I wasn’t spoon-fed hints but interviewers did challenge almost every assumption and asked “why” repeatedly.
I did solve/discuss all questions, but definitely not in a perfectly smooth way. There were some corrections and optimizations during discussion.
What do you guys think realistically? Competitive enough for selection or too inconsistent?
Hello,
My workplace is extremely toxic and more than learning anything, I am dealing with regular chaos of uncertain priorities, changes in workload on regular basis, others taking credit of your work, if you speak up even diplomatically to improve things - leadership doesn't like it and retaliates by holding on promotion, bullying and belittling. HR doesn't care (many have complained in the past). I was passed over for promotion once 3 years back and now again they are dangling promotion carrot in front of me and not taking any action towards it since last 2 months. I even changed my mind to believe that I only have this job to make money, but leadership pulls us into conversations and then plays blame games/belittling in meetings.
I had started applying for new jobs couple of years back, but due to layoffs, I did hold back myself. My main problem is, my energy is completely drained after dealing with daily chaos and blame games and even after work, my mind just replays the conversations in mind. My mental health is the lowest i.e. not as strong as it was when I joined this workplace. I do get couple of hours every day to focus on LC/interview prep, but I lack mental and physical energy. I tried sleeping early and waking up early which helped a bit, but after few mins, my mind starts replaying work conversations in mind.
I have started taking time off of work to focus on prep, but it's not enough. I haven't touched DSA in few years now and even before that it wasn't my favorite, so in current job market, I will have to grind harder on it.
How do you detach yourself from work or anything stressful in general to focus on interview prep?
Full disclosure: I work at Interview Kickstart and helped put this together, so take that for what it is. That said, the content is genuinely free and there's no pitch baked into the event itself.
The panel is called Resurge 2026. It's happening May 12th, 6–8 PM PT. We're covering three things: what the actual 2026 hiring data looks like (jobs are coming back but AI fluency is now basically required), what the AI skill stack looks like by domain, and how interviews have changed at FAANG+ companies in the last 12 months.
Panelists are senior people from Microsoft, Amazon, Instacart, and Expedia — all actively hiring or recently interviewing candidates.
Free to attend, free resources afterward (AI stack guide and self-assessment rubric). Register here if interested: https://interviewkickstart.com/events/resurge2026?utm_source=social&utm_medium=reddit&utm_campaign=L10X_Social_Resurge_reddit9may
We analyzed 3,449 active AI Engineer postings to map the skills companies actually want in 2026: Python, LLMs, RAG, LangChain, AWS, and US base salary.
Two years ago, "AI Engineer" was a fuzzy keyword that could mean almost anything: an ML researcher, a data scientist with a Python script, a backend engineer who fine-tuned a model once. In 2026 it has settled into a much more specific job: take a foundation model, wrap it in retrieval, monitoring, and an API, and ship it into a product. The variance lives in which model provider, which vector store, and which orchestration framework, not in what the work is.
To put numbers on it, we looked at every active AI Engineer posting on the InterviewStack.io job board as of May 2026, 3,449 listings, with skills extracted from descriptions and synonyms collapsed (so gen ai and generative ai count once, gcp and google cloud count once).
The headline: an AI Engineer posting in 2026 is, on average, a Python job plus an LLM job plus a retrieval job plus a cloud job rolled into one. Two skills appear in roughly two-thirds of postings or more, the RAG-plus-LangChain pattern has crossed the common-tier line, and a quiet salary premium has attached itself to anyone who can also handle the distributed-systems work behind those applications.
> Key findings > > - 3,449 active AI Engineer postings analyzed across the live job board as of May 2026. > - Python (71%) and LLMs (66%) are the only two table-stakes skills; 1,821 postings (53%) ask for both together. > - The LLM application stack has moved from differentiator to common: RAG (40%), Generative AI (39%), LangChain (25%), and OpenAI (20%) all now sit in the 20-50% common tier. > - Median US base salary is $146,000 (n=636), one of the highest role medians on our board. > - Distributed-systems and data-platform skills carry the biggest salary premiums: Distributed Systems ($180K, +$34K), Kafka ($171,500, +$25.5K), Apache Spark ($170K, +$24K), and Snowflake ($170K, +$24K). > - Only 6% of postings are entry-level (206 of 3,449); senior plus staff roles together make up 40% of the market. > - The US is 36% of postings, India is 13%: a much US-heavier mix than the Data Engineer market, where India is 23%. > - Onsite is still the default at 50% of postings; 34% are hybrid and 27% are remote (postings can carry multiple tags).
[...continues in full post]
→ Full analysis with charts: https://www.interviewstack.io/blog/ai-engineer-skills-companies-want-2026