Meeting Title: Brainforge AI Engineer Interview Date: 2026-03-30 Meeting participants: Sean, Kaela Gallagher
WEBVTT
1 00:01:39.690 ⇒ 00:01:41.790 Kaela Gallagher: Hey, Sean, how’s it going?
2 00:01:44.410 ⇒ 00:01:47.820 Sean: Hey, Kayla, I am good. How about you? Thanks for asking.
3 00:01:48.420 ⇒ 00:01:57.090 Kaela Gallagher: Doing well, thanks for… for taking the time to meet. Yeah, excited to get to know, like, a little bit more about you, and I can tell you more about, like.
4 00:01:57.200 ⇒ 00:02:00.630 Kaela Gallagher: Brain Forge as well, and kind of what we’re doing.
5 00:02:02.270 ⇒ 00:02:16.180 Kaela Gallagher: I guess just starting off, I know you submitted an application with us, and, you had a LinkedIn link in there, but it’s not working. I’m curious if your profile’s still active.
6 00:02:16.910 ⇒ 00:02:21.780 Sean: It is. It is supposed to be. Let me just double-check really quick.
7 00:02:22.350 ⇒ 00:02:23.200 Kaela Gallagher: Okay.
8 00:02:23.660 ⇒ 00:02:24.260 Sean: So…
9 00:02:25.020 ⇒ 00:02:39.480 Sean: Yeah, I actually, you know, so, these applies. I’m basically looking for a job for about 2-3 days now, and I have basically hired this HR agency that basically does the bidding for you. I think.
10 00:02:39.760 ⇒ 00:02:44.720 Sean: you know, I just want to double-check on that, so… not sure.
11 00:02:47.320 ⇒ 00:02:51.629 Kaela Gallagher: Okay, so maybe they sent, like, the wrong link or something, is what you’re saying?
12 00:02:52.630 ⇒ 00:03:03.319 Sean: maybe that’s the… that’s what happened. Otherwise, I haven’t asked, you know, seen anything like that before. But yeah, I’ll just double check. I’ll send it to you after the chat as well, no worries.
13 00:03:03.910 ⇒ 00:03:12.409 Kaela Gallagher: Okay, okay, yeah, or if you want to drop it in the chat, that would be helpful. I do have a resume in front of me for now, though.
14 00:03:12.670 ⇒ 00:03:19.839 Kaela Gallagher: So I can refer to this, but I guess, yeah, you mentioned you’re on the market, like, curious what’s putting you on the market for something new.
15 00:03:20.270 ⇒ 00:03:24.320 Sean: Sure thing. So, in Tech Cloud, specifically, they do…
16 00:03:24.410 ⇒ 00:03:49.379 Sean: So, they have their full-time engineers that they basically outsource for different projects and products, right? So, I’ve been working on my second project with Tetla right now, and this project is in maintenance mode as of, you know, last week. So, they were kind enough to offer me a benching position that basically have 3 to 4 hours work, you know, a day, still pays you full-time, but I am looking for
17 00:03:49.380 ⇒ 00:04:03.430 Sean: something, you know, more robust in terms of full-time job. So, yeah, that is why I’m in the market. They don’t have any upcoming projects for us specifically, you know, for upcoming quarter or so, so that’s why I’m looking for a way out.
18 00:04:04.410 ⇒ 00:04:10.730 Kaela Gallagher: Okay, okay, got it. And, can you tell me more about the two projects that you’ve done with them?
19 00:04:11.230 ⇒ 00:04:20.710 Sean: Sure thing. So, the first project that I was doing with them was Arena. Now, Arena is basically a project which is,
20 00:04:20.709 ⇒ 00:04:34.579 Sean: you can say education tool, in which you have a whiteboard right next to a chatbot, and what, you know, where the student is learning from is actually an LLM, right? So, what we did is that we created this LLM agnostic,
21 00:04:34.590 ⇒ 00:04:58.340 Sean: you know, adapter, so that any developer can come in and plug in their own LLM. Perhaps they have a local LLM, you know, that is good for tutoring, and then we beta test it within our own organization, and then we basically release it for production usage, and then the student comes in, they use that tutor to learn about stuff, and based on those reviews, we generate revenue that goes directly into the developer’s pocket.
22 00:04:58.340 ⇒ 00:05:01.180 Sean: So, that was basically an idea.
23 00:05:01.180 ⇒ 00:05:19.130 Sean: the greater good this idea was doing is that it was resolving one of the most asked questions, that whether or not, you know, U.S. is ready to move from human tutoring towards AI tutoring. And, you know, that is the data set that we are trying to collect via that usage, so that was the project.
24 00:05:19.130 ⇒ 00:05:20.370 Sean: The second project.
25 00:05:20.370 ⇒ 00:05:45.299 Sean: Yeah, I can dive into the second one as well, but that was more related towards medical field. It was… it basically started just like, you know, you can say our food delivery app, this was medicine delivery app, that’s how we started it. You know, any user can come up with prescriptions, upload their prescriptions, and then have an order placed in their nearby pharmacy, and then it would be getting delivered, you know, to their doorstep. That’s the first part.
26 00:05:45.300 ⇒ 00:06:08.680 Sean: we did. The second part was where we, you know, collaborated with different kind of labs and, you know, added, lab results as well. So now you basically book an appointment, trained physician, you know, trained, you know, personnel, they basically drive towards your home, they take your blood sample, give it back to the lab, and then you can check up those, you know, results up online, within the app.
27 00:06:09.060 ⇒ 00:06:33.519 Sean: There was a third feature as well, which was for consulting purposes, with the use of NLP. We were using a… we were creating a chatbot that basically navigates a patient towards their specific disease. Let’s just say that someone is having a rash, right? And they can upload a photo of that and ask the chatbot, what do you think it is, and how should I, you know, diagnose it and all. It would also refer you a certain
28 00:06:33.520 ⇒ 00:06:40.019 Sean: you know, specialist, that we have on board and stuff like that for a short meeting and stuff, so this was the idea.
29 00:06:40.490 ⇒ 00:06:54.390 Kaela Gallagher: Okay, okay, cool. Can you tell me more about, like, an AI-related project that you have led, whether that is through, like, through, you know, this company, or maybe, like, on the side?
30 00:06:54.890 ⇒ 00:07:18.949 Sean: So both of these projects were AI-focused, right? Just talked about Biomark, which was an LLM-based chatbot that was entirely an AI. The first one that I was talking about was Arena. In Arena, we were using LLMs and LLPs that was also related to AI. Both of these projects I led myself. In the second one, in the latter one, we have two teams, right? One of which is I’m leading.
31 00:07:18.950 ⇒ 00:07:40.390 Sean: And the other one, I’m not, you know, a colleague of mine is leading. So, the first one, I was the sole developer on that, initially, for the first three months, and then we hired two associates, they were onboarded, and then I, you know, started to lead them as well, and mentored them and stuff. So, yeah, both of these are great, you know, examples of me leading a project in AI.
32 00:07:40.930 ⇒ 00:07:55.710 Kaela Gallagher: Okay, okay. And what’s, like, a new… because AI is, like, always evolving and changing, I’m curious, like, what is a new AI tool or skill that you’ve, like, taken initiative to learn recently?
33 00:07:56.200 ⇒ 00:08:09.479 Sean: Sounds good, yeah. So, agreeing with the first point, that yes, AI is continuously, you know, evolving and improving, I won’t go ahead with the improving factor, because I just believe that sometimes it is kind of fixating on that fact.
34 00:08:09.480 ⇒ 00:08:24.660 Sean: But, yeah, yes, it is evolving, and to keep myself up to date, I do use a lot of medium blocks. I am subscribed to all the… you know, I have suspicions to perhaps all the LLMs there are. Major chunk from my salary goes there. So, you know, other than that.
35 00:08:24.690 ⇒ 00:08:30.140 Sean: the latest one that I have used is Gemini. So, Gemini, in the initial
36 00:08:31.650 ⇒ 00:08:55.219 Sean: of 2026, Chev and I basically launched a visual, you know, LLM, so it was a BLM that basically gives you visual support as well as, you know, the tokenized support, which I was really curious about. I did test it out on that a lot, you know, I did use, you know, I did… started creating a couple of plugins with the help of that, plugins that helped you create emojis from, you know, whatever you were thinking of.
37 00:08:55.220 ⇒ 00:09:13.569 Sean: So, it was… I generated some pretty good results initially from using that, and I also used that within Arena as well. The later iteration of that, I asked them to incorporate BLM, because I was not working on that project anymore, but still, you know,
38 00:09:13.570 ⇒ 00:09:17.500 Sean: So, I made a suggestion in that project. But yeah, Gemini would be it.
39 00:09:17.990 ⇒ 00:09:23.810 Kaela Gallagher: Okay, okay, cool. And you’re based in Virginia right now, right?
40 00:09:23.910 ⇒ 00:09:25.330 Sean: Yep, send her a…
41 00:09:26.090 ⇒ 00:09:27.940 Kaela Gallagher: Okay, is that near DC?
42 00:09:28.520 ⇒ 00:09:30.849 Sean: No, it’s, it’s in Eastern.
43 00:09:31.500 ⇒ 00:09:34.869 Kaela Gallagher: Oh, okay, okay, cool.
44 00:09:34.980 ⇒ 00:09:44.339 Kaela Gallagher: I recently visited, like, wine… the wine country of Virginia, kind of on, like, the eastern side as well. It was so pretty.
45 00:09:44.520 ⇒ 00:09:45.460 Kaela Gallagher: Yeah.
46 00:09:46.080 ⇒ 00:09:49.820 Kaela Gallagher: Okay, awesome. And you’re looking mostly for, like, remote positions?
47 00:09:50.070 ⇒ 00:09:51.240 Sean: Yeah, I am.
48 00:09:51.240 ⇒ 00:09:55.220 Kaela Gallagher: Okay. Okay. What kind of compensation range are you targeting?
49 00:09:55.700 ⇒ 00:10:01.270 Sean: I am trying to maintain what I’m currently getting from Techlights, about 125 to 45.
50 00:10:01.390 ⇒ 00:10:08.380 Sean: It varies in a bit, but yeah, I’m trying to retain that, but I’m up for negotiations if, you know, we go further in the line.
51 00:10:09.110 ⇒ 00:10:13.170 Kaela Gallagher: Okay, okay, got it. I will say, so…
52 00:10:13.360 ⇒ 00:10:24.220 Kaela Gallagher: just to tell you a little bit more about Brainforge, but while we’re on the compensation topic right now, our entire team is on a 1099 basis, so, like, independent contracting.
53 00:10:24.220 ⇒ 00:10:33.680 Kaela Gallagher: So just wanted to call that… that out, that’s our work structure, like, we’re not currently sponsoring visas or anything like that, it’s just all on a 1099 basis.
54 00:10:33.710 ⇒ 00:10:36.970 Kaela Gallagher: It… it… does that work for you?
55 00:10:37.350 ⇒ 00:10:38.199 Sean: Yeah, it sure does.
56 00:10:38.200 ⇒ 00:10:38.800 Kaela Gallagher: Okay.
57 00:10:39.750 ⇒ 00:10:44.510 Kaela Gallagher: Okay, perfect. Yeah, and just to tell you a little bit more about Brainforge, so…
58 00:10:44.680 ⇒ 00:11:03.909 Kaela Gallagher: We’re a data and AI consulting company, so we’re not only doing, like, traditional consulting of, like, you know, problem solving and presenting a solution, doing analytics, but then we actually have, like, a hands-on engineering team that’s, you know, going in and implementing, various, like, data and AI solutions.
59 00:11:04.080 ⇒ 00:11:14.620 Kaela Gallagher: Most of our clients are going to be, like, small to mid-sized organizations across a wide variety of industries, like CPG and healthcare, finance.
60 00:11:14.680 ⇒ 00:11:30.750 Kaela Gallagher: As an engineer on the team, you would be working with probably 2 to 3 clients at a time, so really important that we have engineers that can contact switch, and we also have our engineers joining calls with our clients, so the communication aspect is really important, too.
61 00:11:31.050 ⇒ 00:11:43.010 Kaela Gallagher: The roles that we have open right now, we have an AI engineer position, a data engineer position, and an analytics engineer role.
62 00:11:43.210 ⇒ 00:11:51.999 Kaela Gallagher: the data and analytics engineer roles sit within our data service line, our data team, and then obviously the AI role would be on our AI team.
63 00:11:52.280 ⇒ 00:12:03.889 Kaela Gallagher: We’re fully remote right now, we have talent kind of across the globe, but our team does work, US time zones, typically, like, Central or Eastern time zones.
64 00:12:04.180 ⇒ 00:12:09.299 Kaela Gallagher: Any questions? I know I just gave you a lot of information. Any questions for me?
65 00:12:09.790 ⇒ 00:12:20.419 Sean: So, just on top of my mind, one thing that I’m really curious about is the current structure that we have going, right? So, what is the current formation of the team? How many people?
66 00:12:20.720 ⇒ 00:12:23.080 Sean: What kind of hierarchy and stuff?
67 00:12:23.460 ⇒ 00:12:41.109 Kaela Gallagher: Yeah, so we’re about 25 people right now. We have a fairly flat hierarchy. I would say most of the people are communicating directly with our CEOs. However, that is a lot for our CEOs, so we’re trying to adjust that a little bit. We’re bringing in,
68 00:12:41.390 ⇒ 00:12:57.009 Kaela Gallagher: we have service leaders for each of our, delivery lines. So, we have, like, a data service leader, an AI service leader, and then a strategy and analytics service leader, and so they are kind of managing those 3 services that we’re providing to clients.
69 00:12:57.060 ⇒ 00:13:03.649 Kaela Gallagher: We also have something called a CSO, Client Success Owner, who is,
70 00:13:03.790 ⇒ 00:13:13.309 Kaela Gallagher: kind of the main face of Brainforge to our clients, so we’re gonna have a CSO for each client as well. So that’s kind of our structure right now.
71 00:13:13.990 ⇒ 00:13:17.140 Sean: Yeah, you guys have a pod structure kind of thing. That’s good. That’s cool.
72 00:13:17.140 ⇒ 00:13:18.240 Kaela Gallagher: Yeah, yeah.
73 00:13:18.750 ⇒ 00:13:21.479 Sean: No further questions from my side. All good.
74 00:13:21.910 ⇒ 00:13:38.030 Kaela Gallagher: Okay, cool. Just an overview on our interview process. It’s 3 rounds. First one is, a bit more, like, cultural and about your experience, maybe a few technical questions. The second one dives into your technical experience a little bit deeper, but no, like.
75 00:13:38.080 ⇒ 00:13:44.040 Kaela Gallagher: live coding or anything like that. And then the third round is a take-home, like.
76 00:13:44.550 ⇒ 00:13:50.250 Kaela Gallagher: Coding challenge, basically, so you’ll bring your solution to the final… final round and present on it.
77 00:13:50.460 ⇒ 00:13:55.839 Kaela Gallagher: And that’ll be, like, a panel style, so… That’s an overview of…
78 00:13:56.050 ⇒ 00:14:03.940 Kaela Gallagher: Yeah, what we have so far. Just confirming, like, the AI engineer role is the one that, stands out to you the most, right?
79 00:14:05.790 ⇒ 00:14:06.500 Sean: Exactly.
80 00:14:07.060 ⇒ 00:14:08.540 Sean: It does, yes.
81 00:14:08.910 ⇒ 00:14:19.449 Kaela Gallagher: Okay, okay, perfect. Yeah, I can send you, an invite for the first round later today, and then if you have any questions during the process at all, just let me know, I’m happy to help.
82 00:14:20.320 ⇒ 00:14:21.910 Sean: Sounds good, sounds good. Thanks for that.
83 00:14:22.330 ⇒ 00:14:25.219 Kaela Gallagher: Yeah, awesome, thanks for your time, Sean. I appreciate it.
84 00:14:25.610 ⇒ 00:14:27.549 Sean: Likewise, have a good one. Nice catching up with you.
85 00:14:28.120 ⇒ 00:14:29.839 Kaela Gallagher: Bye. Talk to you later. Bye.