u/Sea-Intern-5402

https://preview.redd.it/yqyi8d1cykjh1.jpg?width=5100&format=pjpg&auto=webp&s=418eb107c515c8ec3886d546158aaf17d08d5597

Hi everyone,

I am a Senior Backend Engineer with 8+ years of professional full-time experience specializing in distributed microservices, high-throughput message processing, and cloud-native infrastructure on AWS. I am currently preparing to apply for Senior / L5 software engineering roles at FAANG and top-tier product companies.

Recently, I overhauled my resume to better capture my backend performance optimizations (such as driving down P90 latency significantly) and to incorporate my recent work building applied Generative AI tooling (like RAG pipelines and autonomous agentic incident-triage workflows) into production environments.

What I am targeting & my current situation:

• Target Roles: Senior / L5 Backend Engineer, Distributed Systems Engineer.
• Locations: Remote and US-based hybrid/onsite positions (currently based in the US).
• Current Challenge: I've been actively applying through standard online channels, but I am noticing a major bottleneck getting past initial recruiter screens or automated tracking systems, which prompted me to look into improving my resume structure and impact metrics.

What I would love feedback on:

1. The AI/LLM Bullets: Do my GenAI and RAG bullet points read like genuine, production-grade engineering impact, or do they come across as overly buzzwordy for an L5 profile?
2. Metrics & Impact: Are my scaling and performance metrics (such as the latency reduction and query optimization percentages) clear, compelling, and appropriately framed for an experienced backend engineer?
3. Layout & Formatting: Are there any structural, spacing, or readability red flags ??

Thank you in advance for your time.

Hey everyone,

​I've been seeing a lot of amazing posts here lately about interview experiences and massive compensation packages. It got me wondering about the day-to-day backgrounds of the people landing these roles.

​For those of you getting these top-tier offers:

​What is your primary tech stack? Are you working in traditional SDE roles (Backend, Full-stack, etc.), or are you specifically working in AI/ML?

​Resume Shortlisting: Are you doing any AI-related side projects or adding specific AI tools to your resume to get picked up by recruiters more easily?

Does having AI experience on your resume actually give you a significant edge for getting interview callbacks right now?

​Would love to hear your thoughts and find out what is working best in the current market.

Thanks!

​Hey everyone,

​I’ve been looking at job boards lately and it feels like traditional Software Engineering (SWE) roles have completely dried up. Instead, almost everything requires experience in ML, AI, or LLM integrations.

​But there seems to be a massive disconnect here. More and more data is coming out showing that big companies massively over-invested in the AI hype. Between exploding compute/token bills and agents failing complex tasks, a lot of analysts are pointing out that human engineers are actually far more cost-effective and reliable.

​Despite companies realizing the ROI isn't matching the insane capital spend, the job market still feels entirely skewed toward AI. Traditional full-stack or backend roles feel incredibly scarce.

​For those of you actively interviewing or prepping on LeetCode right now:

​Are you pivoting your prep? Are you pausing standard DS/Algo to try and cram ML concepts, or are you doubling down on core SWE skills hoping the pendulum swings back?

​How are you handling the lack of traditional roles? Are people just applying to ML roles anyway and hoping their foundational coding carries them through?

​Would love to hear how you all are navigating this right now.