Meeting Title: Brainforge AI Engineer Interview Date: 2026-04-01 Meeting participants: Vimarsh Patel, Kaela Gallagher
WEBVTT
1 00:06:21.390 ⇒ 00:06:22.010 Vimarsh Patel: Hello.
2 00:06:22.670 ⇒ 00:06:25.490 Kaela Gallagher: Hi, I’m from Orange, how’s it going?
3 00:06:25.490 ⇒ 00:06:26.860 Vimarsh Patel: I’m good, how about you?
4 00:06:26.860 ⇒ 00:06:38.919 Kaela Gallagher: Doing well. Sorry I’m a couple minutes late. I’m working, actually, in person with some other Brainforge people right now, and so my technology setup’s a little bit different than usual.
5 00:06:38.920 ⇒ 00:06:40.420 Vimarsh Patel: Some noise out of the noise.
6 00:06:40.930 ⇒ 00:06:46.890 Kaela Gallagher: Yeah, well, I’m excited to get to know a little bit more about you.
7 00:06:46.890 ⇒ 00:06:47.530 Vimarsh Patel: if…
8 00:06:47.530 ⇒ 00:06:54.659 Kaela Gallagher: I noticed we have a mutual connection on LinkedIn, and curious, how you might know Ed Bates.
9 00:06:55.980 ⇒ 00:07:04.120 Vimarsh Patel: I don’t think, I’m not sure who the mutual connection is, adweet and Joshua.
10 00:07:04.750 ⇒ 00:07:20.230 Vimarsh Patel: I think I’m just connected… I’m just… I think I’m just connected with them on LinkedIn, just, like, for networking purposes. That’s the only way I think I would know them. I have not met or interacted with them. Mostly it would just be, you know, like, some LinkedIn post or something like that, we must have connected, and yeah, stayed over.
11 00:07:20.230 ⇒ 00:07:21.750 Kaela Gallagher: Okay, okay.
12 00:07:21.750 ⇒ 00:07:24.490 Vimarsh Patel: I don’t know them personally, I don’t know them personally, so…
13 00:07:24.970 ⇒ 00:07:27.890 Kaela Gallagher: Yeah, no worries. The reason I was,
14 00:07:28.020 ⇒ 00:07:34.940 Kaela Gallagher: asking is because Invate recently joined the team, so, was curious if you guys knew each other, but.
15 00:07:34.940 ⇒ 00:07:35.960 Vimarsh Patel: No, no, no.
16 00:07:36.360 ⇒ 00:07:50.040 Kaela Gallagher: Okay, no worries. Well, yeah, I came across, your profile, and yeah, I thought your background could be, of interest to our team. We’re currently hiring an AI engineer, and
17 00:07:50.040 ⇒ 00:07:57.890 Kaela Gallagher: Yeah, would love to just start off, like, learning a little bit more about you. Curious, like, why you’re open to new opportunities.
18 00:07:58.140 ⇒ 00:08:14.919 Vimarsh Patel: Sure. Just to mention, like, now that I check it, like, I see that Advya and I went to the same undergrad university, so I think that’s how we were connected in the first place. He was my senior, but, like, I don’t think I remember talking to him, but, like, we just, like, it’s the same university, so I think the LinkedIn recommendations would have come up, and we must have connected, so…
19 00:08:14.920 ⇒ 00:08:16.979 Kaela Gallagher: Okay, okay, cool, awesome.
20 00:08:16.980 ⇒ 00:08:34.039 Vimarsh Patel: But yeah, and more about my background. So, yes, so, like, yes, you are looking for an AI engineer. I have… I’ve been trying to build myself for the same position for the past couple of years. Even in my current role, I am building automation workflows and, like, trying to
21 00:08:34.039 ⇒ 00:08:37.609 Vimarsh Patel: incorporate LLM and agents into the…
22 00:08:37.700 ⇒ 00:08:42.269 Vimarsh Patel: The existing systems that my current employer has, and…
23 00:08:42.400 ⇒ 00:08:50.570 Vimarsh Patel: I think when you sent over the role, I did, I find it… I found it exciting, and that is why I did complete the application.
24 00:08:50.800 ⇒ 00:08:58.670 Vimarsh Patel: So… and, like, if there’s something specific, then I can definitely, like, talk more about it once you ask me something, maybe around the lines, I can be much more specific about that.
25 00:08:58.980 ⇒ 00:09:04.330 Kaela Gallagher: Yeah, yeah, yeah. Can you tell me more about the tools you’re building in your current role, like the AI tools?
26 00:09:04.510 ⇒ 00:09:21.580 Vimarsh Patel: Yes, sure. So, basically, I’m trying to, build a reconciliation system for them, like an automated system, like, I’m trying to automate the HR manual process that they have, across a three-way system. Basically, they have, like, their QuickBooks for invoicing, they have their,
27 00:09:21.580 ⇒ 00:09:29.750 Vimarsh Patel: employee timesheets in ADP, and then they get the approved hours from their clients through the email. So I’m trying to automate that whole process, like.
28 00:09:29.810 ⇒ 00:09:45.150 Vimarsh Patel: ingest all the three data sources, normalize them, cross-reference them, and then, basically, I have a set of rules that help me reconcile the outputs, and the LLM is basically used for
29 00:09:45.150 ⇒ 00:10:01.940 Vimarsh Patel: like, for extraction of data and marking… just for marking, like, if it is, like, what the issue was. The whole system in itself is based… rules-based right now, because it’s, like, quite initial. So, and what I’m using is mostly… my backend is based on Python.
30 00:10:02.250 ⇒ 00:10:12.270 Vimarsh Patel: my, I’m using FastAPIs. For LLM, I’m using Claude. I’m trying to, integrate, like, some of the
31 00:10:12.610 ⇒ 00:10:12.985 Vimarsh Patel: the
32 00:10:13.440 ⇒ 00:10:27.079 Vimarsh Patel: workflows that are specific, they are automated by code, like a schedule trigger, or like a… like when a new timesheet comes in. But apart from that, I’m… I am integrating two agents into it using Langraph.
33 00:10:27.080 ⇒ 00:10:38.100 Vimarsh Patel: That can help orchestrate the whole system on its own. So, like, there is no human required, and it can send, like, as soon as the reports are ready, bi-weekly, whenever the…
34 00:10:38.100 ⇒ 00:10:41.560 Vimarsh Patel: The payment schedule is done, like, whenever the payroll runs.
35 00:10:41.560 ⇒ 00:10:46.419 Vimarsh Patel: The agents will be triggered, and they will reconcile, and then send the report back to the human.
36 00:10:48.380 ⇒ 00:11:00.269 Kaela Gallagher: Okay, okay, awesome. Can you tell me a little bit more about your passion for AI? Like, maybe something about AI that you’ve done on your own outside of work?
37 00:11:00.900 ⇒ 00:11:19.949 Vimarsh Patel: Yes, so I am trying to build a voice agent. Basically, that’s what I’m doing on the side. I am trying to learn how voice agents work, and I’m trying to, like, I have a, like, a family friend who runs a restaurant, so I’m trying to help place that voice agent as their… as their main source of ordering.
38 00:11:20.020 ⇒ 00:11:38.259 Vimarsh Patel: like, so I’m trying to, build it, build that, and maybe test it out with real users out there in that restaurant. So, I’m using the WAPI, voice, WAPI’s like a startup who builds voice agents, so I’m trying to use that, and I have a RAG pipeline built for…
39 00:11:38.460 ⇒ 00:11:41.349 Vimarsh Patel: Like, for ingestion of the menu items.
40 00:11:41.510 ⇒ 00:11:59.920 Vimarsh Patel: And, like, if you call, then it will identify if you’re an existing customer or not, and if you’re an existing customer, it will pull up your details, look at your past orders, and try to maybe ask you that if you want to repeat your previous order, or maybe recommend you something similar to what you have already ordered before.
41 00:11:59.920 ⇒ 00:12:09.590 Vimarsh Patel: will know your preferences, and so I’m trying to build that. I know it’s almost there, I am, like, it works out good in testing, but now I want to use it out on real users.
42 00:12:09.590 ⇒ 00:12:23.969 Vimarsh Patel: So, I’m trying to, like, talk with my friend, and we are trying to implement it some way. Like, maybe we can get 4 or 5 customers to use that new number to order, like, place an order through that system. So, yeah, that’s something that I’m doing on the side.
43 00:12:24.940 ⇒ 00:12:29.539 Kaela Gallagher: Okay, okay, awesome. And where… where are you based right now?
44 00:12:29.760 ⇒ 00:12:32.880 Vimarsh Patel: I am currently in New Jersey. Hillsborough, New Jersey.
45 00:12:33.200 ⇒ 00:12:38.159 Kaela Gallagher: Okay, okay, got it. Are you open to, like, relocation in the future, or are you.
46 00:12:38.160 ⇒ 00:12:38.530 Vimarsh Patel: Are you staying?
47 00:12:38.530 ⇒ 00:12:39.600 Kaela Gallagher: and… okay.
48 00:12:39.600 ⇒ 00:12:42.590 Vimarsh Patel: No, no, I’m quite open to do that, that’s not an issue.
49 00:12:43.330 ⇒ 00:12:49.279 Kaela Gallagher: Okay, cool. And then what about, the compensation that you’re targeting? What’s the range you’re looking at?
50 00:12:49.660 ⇒ 00:12:56.760 Vimarsh Patel: I don’t think I have a, like, an exact number in mind, but maybe something around, like, 100K, 120, something in that ballpark.
51 00:12:57.610 ⇒ 00:13:11.970 Kaela Gallagher: Okay, okay. I guess just to tell you, like, a little bit more about Brainforge, the reason why I asked about relocation, we are currently fully remote. We have team members all across the globe.
52 00:13:11.970 ⇒ 00:13:23.549 Kaela Gallagher: However, there are quite a few of us in LA right now, that’s where I’m co-working, and we have some people in Austin as well, so we kind of naturally have these little, like, hubs.
53 00:13:23.580 ⇒ 00:13:32.779 Kaela Gallagher: popping up, and so it’s definitely, like, a plus for us if people are located in LA or Austin, or willing to, like, relocate there, so that’s just why I ask.
54 00:13:32.780 ⇒ 00:13:33.100 Vimarsh Patel: I’d be.
55 00:13:33.100 ⇒ 00:13:33.630 Kaela Gallagher: Thank you.
56 00:13:33.630 ⇒ 00:13:36.479 Vimarsh Patel: I love LA. I’ve been there once, and I love the city.
57 00:13:36.480 ⇒ 00:13:39.530 Kaela Gallagher: Cool. Okay, awesome, awesome.
58 00:13:39.530 ⇒ 00:13:58.410 Kaela Gallagher: Yeah, and then just to tell you, like, a little bit more about the AI engineer role, so right now, we… so we’re an AI and data consulting firm. We have three, service lines that we offer our clients. One is data, one’s AI, and one is strategy and analytics.
59 00:13:58.410 ⇒ 00:14:06.580 Kaela Gallagher: oftentimes there’s a combination of these teams, serving our clients, but this role would sit within our AI team.
60 00:14:06.580 ⇒ 00:14:23.350 Kaela Gallagher: Looking for somebody that is a super strong communicator, that’s probably the biggest challenge we’ve had in, hiring for this position, because our engineers are, on calls with our clients, so we need somebody that can explain technical concepts really well.
61 00:14:24.830 ⇒ 00:14:43.350 Kaela Gallagher: In addition to that, because we are consulting, and you would be working with probably, two to three companies at a time, we, would need you to be able to context switch pretty quickly, and, like, be flexible, basically, in, hopping around to different projects. So.
62 00:14:43.350 ⇒ 00:14:43.850 Vimarsh Patel: Incorporated.
63 00:14:43.850 ⇒ 00:14:49.649 Kaela Gallagher: That’s kind of what we are looking for. Any questions for me that I can help answer?
64 00:14:50.100 ⇒ 00:14:56.070 Vimarsh Patel: Yup, what about, like, sponsorship issues or anything related to that? Because I’m on STEM OPT.
65 00:14:57.140 ⇒ 00:15:08.279 Kaela Gallagher: Okay, yeah, so right now, all of our employment is structured on a 1099 basis, so you would be an independent contractor. We do not offer, sponsorship.
66 00:15:08.500 ⇒ 00:15:11.809 Kaela Gallagher: I think that you are still able to work.
67 00:15:11.990 ⇒ 00:15:18.650 Kaela Gallagher: with us on a STEM OPT, but yes, we just can’t, I guess provide sponsorship.
68 00:15:18.650 ⇒ 00:15:21.669 Vimarsh Patel: I just wanted to know, like, if you would be fine with me working on STEM OPT.
69 00:15:22.200 ⇒ 00:15:22.930 Kaela Gallagher: Yes.
70 00:15:23.180 ⇒ 00:15:34.419 Vimarsh Patel: That’s okay. And since you… and you also mentioned that you have employees all over the globe, so I think even if I’m not in the US at some point, you can… like, it can work out, right? It doesn’t have to be, like, a country-based employment.
71 00:15:34.740 ⇒ 00:15:36.300 Kaela Gallagher: Yes, exactly.
72 00:15:36.300 ⇒ 00:15:42.820 Vimarsh Patel: So yeah, that works out for me. Yeah, that’s the only concern I had. I just didn’t want to come up later, it’s better to get it cleared in the beginning.
73 00:15:43.230 ⇒ 00:15:56.800 Kaela Gallagher: 100%, agreed. Yeah. And then just a little overview of our interview process. It’s three stages. The first one is, just kind of an initial get to know you, a kind of overview of your experience.
74 00:15:57.200 ⇒ 00:16:00.410 Vimarsh Patel: Is this considered that, or after this, it will be…
75 00:16:00.760 ⇒ 00:16:09.049 Kaela Gallagher: After this. Yes. And then we have more of, like, a technical round for the second round.
76 00:16:09.370 ⇒ 00:16:25.099 Kaela Gallagher: that is not, like, any sort of live coding or anything, but just kind of, like, deep diving into more technical concepts. And then the final round, we give you a takes home challenge, and you get to…
77 00:16:25.550 ⇒ 00:16:36.290 Kaela Gallagher: bring your solution to, like, the final channel, and they’ll, they’ll, like, dig into your code a little bit further. So, that’s the structure moving forward.
78 00:16:36.290 ⇒ 00:16:39.469 Vimarsh Patel: Perfect, perfect. That sounds really interesting, and…
79 00:16:39.830 ⇒ 00:16:44.910 Vimarsh Patel: This is, like, yeah, it’s pretty interesting. I would definitely love to be a part of the company.
80 00:16:45.040 ⇒ 00:16:46.860 Vimarsh Patel: And we’ll be coming, yeah.
81 00:16:47.270 ⇒ 00:17:02.179 Kaela Gallagher: Yeah, awesome. In terms of the interview process, we can move as quickly as, basically you schedule, because we’ll send you booking links, and then, yeah, the sooner you schedule, like, each round, the quicker we can make through the process.
82 00:17:02.180 ⇒ 00:17:08.259 Vimarsh Patel: I am definitely… if you give it to me, if you send me the link over now, I can schedule for… I’m all up for it.
83 00:17:08.849 ⇒ 00:17:09.449 Kaela Gallagher: Okay.
84 00:17:09.669 ⇒ 00:17:12.259 Kaela Gallagher: Perfect, yeah, I’ll get that sent over shortly, then.
85 00:17:12.490 ⇒ 00:17:16.030 Vimarsh Patel: Perfect, perfect. Sounds amazing. This was great. Thank you, thank you so much for reaching out.
86 00:17:16.190 ⇒ 00:17:18.129 Kaela Gallagher: Yeah, thank you for your time.
87 00:17:18.359 ⇒ 00:17:19.129 Vimarsh Patel: Yes, no worries.
88 00:17:19.130 ⇒ 00:17:19.589 Kaela Gallagher: Alrighty.
89 00:17:19.599 ⇒ 00:17:21.249 Vimarsh Patel: I hope you have a good day, and I’ll…
90 00:17:21.250 ⇒ 00:17:24.239 Kaela Gallagher: You too. Talk to you later. Alright, bye.