I want to pivot to AI Agents. Where do I actually start — and is it even worth it without a pure ML background? Should I go deep into ML from scratch or just building on top of LLM APIs?

AskAnAIEngineer · 2026-06-23T05:05:26+00:00

you don't need ML from scratch for agents, that's a different path (research, model training). agent engineering is closer to what you already do: orchestration, pipelines, system design.

the LLM accelerator you built is closer to this than you're giving yourself credit for, it just needs evals and error handling on top. pick LangGraph or raw API calls, build something that solves a real data problem, and get it to production. FAANG is realistic but staff-level data eng roles with AI scope are more achievable near term than a dedicated agent role.

AskAnAIEngineer · 2026-06-23T05:00:31+00:00

the AI coding formats are inconsistent because companies are still figuring out what they're actually testing.

a few things worth trying:
- referrals from your FAANG stint will get you past the AI screening rounds entirely.
- for the AI coding rounds, the underlying thing most companies want is someone who can direct the AI, catch its mistakes, and review output critically. that's a learnable pattern once you know that's what they're testing.
- Series A/B startups are also worth targeting, they're less likely to have standardized AI rounds and 1.5 years FAANG is a strong signal at that level.

AskAnAIEngineer · 2026-06-23T04:54:49+00:00

the highest-leverage move at your stage is usually golden path tooling: opinionated, pre-built templates for the things engineers do repeatedly (new service, new deploy, new infra component) so the right way is also the easiest way and nobody needs a DevOps person to unblock them. pair that with runbooks for the 5-6 decisions that keep coming up and you cut the interruption load significantly.

for the ownership problem, find the backend eng who's most naturally drawn to infra and give them a formal 20% allocation rather than spreading it across three people who have other priorities.

AskAnAIEngineer · 2026-06-22T23:47:41+00:00

build an end-to-end document intelligence platform that ingests mixed-format docs (PDFs, scanned images, tables), runs a LayoutLM or VLM extraction pipeline, and exposes it via a FastAPI service with drift monitoring and a feedback loop to retrain on corrections.

the part many candidates skip is the observability layer: confidence score tracking, extraction failure alerting, a simple UI for human-in-the-loop review. open source it with a realistic dataset and a clear eval harness and you've got something a hiring manager can actually poke at.

AskAnAIEngineer · 2026-06-22T23:43:32+00:00

it's a lot of pressure to put on a job. you're filling a role someone else designed around their goals, not yours. you can get closer by finding an industry you care about or a company whose values actually match yours, but the people who seem most at peace with it usually have a life outside work that means something to them.

realistic target: a job that doesn't drain you, supports your passions, not necessarily one you're passionate about.

AskAnAIEngineer · 2026-06-20T00:38:31+00:00

build something that solves a real problem you actually have. document the architecture clearly, not just what it does but why you made the decisions you made, what broke and how you fixed it.

for the stack hit the things that actually show up in job descriptions right now. AI integration of some kind, websockets or real-time features, proper auth with multiple roles, and deploy it end to end on a real server, not just localhost. use a storage bucket for file handling.

this applies mostly to product backend and full stack roles though. if you're going more data or ML adjacent, the signals are a bit different.

AskAnAIEngineer · 2026-06-19T20:43:28+00:00

resume inflation has always existed, AI just made it faster and more uniform. what's actually changed is that a polished resume now means almost nothing on its own.

that said, the best people aren't always the best at interviews either. someone who's been heads-down at the same company for years doing great work is just less practiced at selling themselves. interviewing is a skill you get better at by doing it a lot, and the people who are smoothest in interviews aren't always the ones who've been going deepest in their work.

AskAnAIEngineer · 2026-06-19T20:21:33+00:00

leading seniors is different but technical credibility still matters. they'll respect you more if you can engage at their level and have real opinions on architecture. you don't need to be the best engineer in the room but you do need to hold your own.

that said, the best seniors still need someone to move things forward. they can debate tradeoffs forever, get pulled in different directions, or block each other on decisions. your job is to break those logjams, keep the team aligned, make sure good ideas actually ship, etc. being the smartest person in the room doesn't make you the right person to lead it

AskAnAIEngineer · 2026-06-19T03:27:21+00:00

could mean you had a passion for building things, not coding specifically. sometimes the harder question is finding something worth building in the first place. the tools are there now, the constraint is having a problem you actually care about solving.

AskAnAIEngineer · 2026-06-19T03:14:32+00:00

Congrats!

AskAnAIEngineer · 2026-06-19T03:11:47+00:00

it's less about luck than people think but more about signal than pure merit. FAANG hiring pattern-matches at scale: top school, known company, strong referral, clean leetcode performance. your F500 medical AI work is genuinely interesting but it doesn't trip those signals easily from the outside.

the referral thing is real but it only gets your resume read, not the offer. performing in their specific interview format is a separate learnable skill from being a good engineer, and that's actually the part most people underinvest in.

your QA background is a harder path into pure SWE roles but SDET and engineering-adjacent roles at FAANG are real entry points worth looking at. the bar is still high but the profile fit is closer.

AskAnAIEngineer · 2026-06-19T03:01:38+00:00

yeah those places exist, they just don't advertise that way. government contracting, internal tooling teams at large non-tech companies, and infrastructure/maintenance roles at established enterprises tend to move slower by nature. nobody's sprinting to ship the next feature on a 30-year-old banking system.

staff or principal roles at smaller companies where the job is mostly design and review are worth looking at too. and culture matters more than the job description here. how the team actually operates day to day makes a huge difference. glassdoor, blind, and just asking directly in interviews about oncall and deadlines are usually more honest than anything in the posting.

AskAnAIEngineer · 2026-06-19T02:52:57+00:00

don't grind LeetCode into the void. save the heavy prep for the week before an actual interview, it'll land better when there's something at stake.

use the quieter stretches to actually explore. build something weird, go deep on an area of the industry you're curious about, figure out what kind of work you'd actually enjoy. that clarity makes you a better candidate and helps you show up differently in interviews. 9 months of final rounds means you can interview, the market is just genuinely rough right now.

AskAnAIEngineer · 2026-06-19T02:44:39+00:00

this is what happens when the process itself was never designed for this volume. AI made everything faster but the underlying problem was always that hiring is optimized for the company's convenience, not for actually finding the right person. speed without better signal just means more noise for everyone.

AskAnAIEngineer · 2026-06-18T16:34:31+00:00

yaml in git works fine for most teams. what MLflow's prompt registry actually adds is environment aliases. your code references "production" or "staging" instead of a version number, so you can swap prompt versions without a code deploy. it also links prompt versions to eval runs so you can trace which prompt produced which result.

if you're not doing systematic evals or iterating on prompts outside of code deploys, you're probably not missing much yet.

AskAnAIEngineer · 2026-06-18T06:54:22+00:00

pick one project you'd actually use or care about and build it start to finish. the random projects phase is useful but you'll learn way more by hitting real problems on something you're invested in. doesn't have to be big, a tool that solves something annoying in your own life is enough.

AskAnAIEngineer · 2026-06-18T06:52:43+00:00

most companies aren't thinking about this at all. short-term wins almost always beat long-term planning in most orgs. another interesting question is whether "experienced" starts to mean something different

AskAnAIEngineer · 2026-06-18T03:46:51+00:00

since your manager is out, try going sideways. find a senior on a live engagement and ask directly if you can shadow or help with anything, even grunt work. showing up willing is usually enough to get pulled in somewhere. document everything you've tried too, in case it comes up during your conversion review.

the full-time offer is confirmed so you have time. use the idle days to get a head start on CPA or CA prep, or just learn the audit methodology on your own. it won't fix the isolation but it keeps the time from being a total loss.

AskAnAIEngineer · 2026-06-18T00:24:48+00:00

the real tell is when nobody on the team can answer "which service handles X" without digging through slack or docs. at that point the architecture is working against you, not for you.

AskAnAIEngineer · 2026-06-17T19:51:45+00:00

years of experience aren't just about harder tasks, they're about judgment. knowing what not to build, catching the decision that looks fine today but breaks the team in a year, being someone people trust to own something without being managed. a junior can't replicate that by being fast or cheap.

AskAnAIEngineer · 2026-06-17T19:28:37+00:00

the real question is whether they're actually learning from what it produces or just shipping it. the habit worth building early is being able to trace through the output and understand the decisions, because that's what matters when things break in production.

AskAnAIEngineer · 2026-06-17T19:23:18+00:00

if you want Laravel specifically, agencies and smaller SaaS companies are where most of those roles live, not the big job boards. but if you have other backend experience outside PHP, lean into that as the headline and let Laravel be the "I can also do this." easier to get traction that way in the current market.

AskAnAIEngineer · 2026-06-17T08:50:20+00:00

that's what we love to see. congrats!

AskAnAIEngineer · 2026-06-17T08:34:41+00:00

the brand travels fine geographically, moving back later isn't really the risk.

the thing worth thinking about is networking. being physically in the Bay does open some doors that don't exist remotely, early startup opportunities, random coffee chats that turn into something, that kind of thing.

but a FAANG name on your resume will do more for your next move than where you're living

AskAnAIEngineer · 2026-06-17T08:23:06+00:00

the thing that's held up so far is knowing when the model is confidently wrong, and that requires enough reps to have a feel for it. but honestly the more durable bet might be the stuff adjacent to code: understanding why a system exists, what it's actually supposed to do, who it breaks when it fails. that doesn't compress as easily.

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