Meeting Title: Brainforge AI Engineer Interview Date: 2026-03-17 Meeting participants: Emma Morgan, Kaela Gallagher
WEBVTT
1 00:00:59.070 ⇒ 00:01:01.239 Kaela Gallagher: Hey, Emma, how’s it going?
2 00:01:02.340 ⇒ 00:01:03.810 Emma Morgan: Hey, how are you?
3 00:01:04.150 ⇒ 00:01:19.279 Kaela Gallagher: Doing well! Thanks for taking the time to meet with me, appreciate it. I know you were interested in one of our openings, so yeah, just excited to learn a little bit more about you and, what you’re looking for next.
4 00:01:19.400 ⇒ 00:01:26.139 Kaela Gallagher: Would love to just start our conversation by asking, like, what is putting you on the market for a new opportunity?
5 00:01:27.500 ⇒ 00:01:46.549 Emma Morgan: Yeah, so, in my current role, I have, like, two projects. So, one of them is on which, like, I’m leading, and one of them on which, like, I’m actively coding on. So, the project on which I’m quoting on, the client… that project is basically… will be completed in the coming 2 months.
6 00:01:46.550 ⇒ 00:02:00.260 Emma Morgan: So, the client would be winding up that project. So that’s why, I’m looking for some similar rules for which I have experience with. That brings me to the grant market.
7 00:02:00.880 ⇒ 00:02:04.719 Kaela Gallagher: Okay, okay, and is that your role with Open Tech Strategies?
8 00:02:04.720 ⇒ 00:02:05.609 Emma Morgan: Yes, yes.
9 00:02:05.610 ⇒ 00:02:09.629 Kaela Gallagher: Okay, okay, got it. So you’ve been with them for 6 years now?
10 00:02:09.940 ⇒ 00:02:23.599 Kaela Gallagher: Yep, yes. Okay, okay, got it. Are you… I know you mentioned the project is, like, ending in 2 months. Are you open to starting a new position before then, or are you wanting to wait until May?
11 00:02:24.400 ⇒ 00:02:39.099 Emma Morgan: Yeah, so, currently, like, my, notice period would be 2 weeks, so I can, if it’s, like, within these 2 months, then I can start within, like, 2 weeks, but if it’s after the 2 months that I… then I can start immediately.
12 00:02:39.500 ⇒ 00:02:48.640 Kaela Gallagher: Okay, okay, that’s… that’s helpful to know, just your… your timeline. Can you tell me more about the work that you’ve been doing with your current company?
13 00:02:49.260 ⇒ 00:03:06.849 Emma Morgan: Yeah, sure. So, I have been, working with, Python, and I have experienced, like, for almost over 7 plus years, 8 plus years. So one of the things that I do is, like, I have, two projects,
14 00:03:06.850 ⇒ 00:03:14.930 Emma Morgan: on which I’m currently doing work. One of the projects is, which I told you I’m managing a team of 4-5 developers,
15 00:03:15.310 ⇒ 00:03:29.300 Emma Morgan: that was the project, basically, like, 6… 5 years ago, I started from scratch, so we had another opportunity on another project, so I was, so I’m doing a mentorship on that previous project now.
16 00:03:29.300 ⇒ 00:03:46.030 Emma Morgan: So, if I tell you about my projects that I’m working on, so I have one of the projects which is a construction-based application, LLM-based features, which I have, and then the other one is basically a healthcare domain project.
17 00:03:46.300 ⇒ 00:04:06.050 Emma Morgan: For which I’m working. So, on the project on which I’m actively coding on, the front end is on React, and the backend is basically Python. And then I have used, different kind of APIs. We have integrated AI and ML into our projects. We have used AI agents, microservices.
18 00:04:06.050 ⇒ 00:04:27.509 Emma Morgan: That kind of stuff in one project. And the healthcare one, it’s basically, in Angular, and the backend is, like, AIML technologies using Python. And on the DevOps side, I’m using Azure, and AWS as well. I have used AWS in a few previous projects as well.
19 00:04:27.510 ⇒ 00:04:33.589 Emma Morgan: And then on the database side, we are working with Postgres,
20 00:04:34.140 ⇒ 00:04:37.850 Emma Morgan: receiving and getting debtors and all that.
21 00:04:37.850 ⇒ 00:04:54.779 Emma Morgan: And then, if I talk about, I have been using and executed automated background deployment, using Django, and then, I have used different libraries in the projects. We have built code from scratch.
22 00:04:54.780 ⇒ 00:05:07.730 Emma Morgan: My role in one of the projects is basically to, mentor the juniors, and then, I’m consulting with the business analysts to understand the client requirements as well.
23 00:05:07.730 ⇒ 00:05:12.000 Emma Morgan: So, this is all what’s going on with the experience right now.
24 00:05:12.300 ⇒ 00:05:22.390 Kaela Gallagher: Okay, okay, awesome. And I know you mentioned on one of those projects, you have some LLM features. Can you tell me, like, what, I guess at a high level, like, what those are doing?
25 00:05:23.330 ⇒ 00:05:32.920 Emma Morgan: Yeah, sure. So, basically, it’s a PR, based project. So, basically, we are working in different, you know,
26 00:05:32.920 ⇒ 00:05:56.630 Emma Morgan: dashboards. We have our client dashboard for the construction application. So, we have been using a robust application, and, the client’s basically using those LLMs, for time management, for, using, it’s basically a time app for them, and then we have a lot of payments going on and coming in in that project, because it’s a construction-based application, and
27 00:05:56.630 ⇒ 00:06:16.329 Emma Morgan: It basically handles the contracts, it basically handles how the contract is being done from another third party, and then, it basically handles the track costs, everything related to construction, the material, the quantity, and everything. So that, that’s, that’s a whole LLM project.
28 00:06:16.460 ⇒ 00:06:21.749 Kaela Gallagher: Okay, okay, got it. Have you done any other, like, AI or LLM projects?
29 00:06:22.330 ⇒ 00:06:44.410 Emma Morgan: I have done more AI projects. This, there was one of the OCR projects that we did, based on the AI agents. So basically what that project did was we had to grab an invoice, and the structure of the invoice would not be same throughout, because we have different clients, and each client would have a different kind of invoice.
30 00:06:44.410 ⇒ 00:07:02.529 Emma Morgan: So, the AI agent would grab that invoice and extract data from that invoice and send it to one of our APIs to upload that data into a third-party application, which was our desk. So, that was one of the applications where I used LLMs and AIML.
31 00:07:02.530 ⇒ 00:07:06.190 Emma Morgan: agentic, applications.
32 00:07:06.510 ⇒ 00:07:12.119 Kaela Gallagher: Okay, okay, cool. Where… where are you currently based?
33 00:07:12.400 ⇒ 00:07:15.150 Emma Morgan: So I’m in Elk Grove, California, right now.
34 00:07:15.690 ⇒ 00:07:17.340 Kaela Gallagher: You said Elk Grove?
35 00:07:17.540 ⇒ 00:07:18.210 Emma Morgan: Yes.
36 00:07:18.340 ⇒ 00:07:20.210 Kaela Gallagher: Okay, is that near the bay?
37 00:07:21.080 ⇒ 00:07:23.670 Emma Morgan: Yes. Okay.
38 00:07:24.150 ⇒ 00:07:38.810 Kaela Gallagher: Okay, okay, got it. I’m in LA, so familiar in general with California, but not, like, the specific, suburbs of the Bay Area. Okay, cool, just to tell you, like, a little bit more about Brainforge and the role that we’re hiring for right now.
39 00:07:38.810 ⇒ 00:07:50.170 Kaela Gallagher: We are a data and AI consulting company, so we work with a variety of clients across different industries, mostly, like, small to medium-sized companies that we’re partnering with.
40 00:07:50.220 ⇒ 00:08:14.550 Kaela Gallagher: We are hiring a couple engineers right now, one of which being the AI and automation engineer that you applied for. We are looking for engineers who can communicate really well with clients and are comfortable interfacing with clients, because we’re about a 25-person organization right now. We need people to be able to do, like, a lot of different things, and
41 00:08:14.550 ⇒ 00:08:19.030 Kaela Gallagher: Have a background working on a variety of things as well.
42 00:08:19.160 ⇒ 00:08:38.219 Kaela Gallagher: Right now, for our positions, we’re hiring, 1099 contractors, so we are unable to sponsor visas under, under that setting. So, that’s kind of the overview of the role, curious if it’s, like, something that you’re interested in.
43 00:08:38.820 ⇒ 00:08:43.080 Emma Morgan: Yep, I’m comfortable with what you have presented.
44 00:08:43.539 ⇒ 00:08:57.399 Emma Morgan: And, and I think I am more comfortable doing 1099 roles at this moment. Like, recently, I’m also working as a contractor with OpenTech Strategy since, like, 6 years.
45 00:08:57.430 ⇒ 00:09:05.519 Emma Morgan: So, I was… I’m okay with full-time, but more towards… I’m, like, more towards 1099 roles, so that’s perfect.
46 00:09:06.030 ⇒ 00:09:23.820 Kaela Gallagher: Okay, okay, got it. And you don’t require, like, to go through, like, corp to corp or anything like that? Like, you could work directly on 1099? Okay. Yes. Okay. Cool. I think those are all the questions I have for you. Do you have anything for me?
47 00:09:24.360 ⇒ 00:09:28.850 Emma Morgan: Yeah, I just wanted to know that how is the timeline, how…
48 00:09:29.050 ⇒ 00:09:36.759 Emma Morgan: is the timeline for, you know, the hiring process, and when should I expect to hear back from you, either yay or nay?
49 00:09:37.100 ⇒ 00:09:50.599 Kaela Gallagher: Yeah, you can expect to hear back from me, I would say, like, by end of tomorrow. Typically, when I have these conversations, like, I review, candidate profiles and, like, my notes from the call with our team.
50 00:09:50.680 ⇒ 00:10:05.089 Kaela Gallagher: We are moving pretty quickly. We actually already have people in final round stages for… for this role. We’re looking to extend an offer, I would say, like, this week or next. So definitely moving quickly here.
51 00:10:05.270 ⇒ 00:10:06.010 Kaela Gallagher: Yeah.
52 00:10:07.130 ⇒ 00:10:08.620 Emma Morgan: Yeah, sounds good.
53 00:10:09.210 ⇒ 00:10:16.369 Kaela Gallagher: Okay, cool. Yeah, any, any other, like, questions I can help answer?
54 00:10:17.030 ⇒ 00:10:29.959 Emma Morgan: For now, I don’t have any questions. It was just related to how the timelines looked like, or, other than that, if I’ll have any question in mind, I’ll reach you out.
55 00:10:30.320 ⇒ 00:10:37.089 Kaela Gallagher: Okay, alrighty, cool, sounds great. Thanks so much for your time today, I appreciate it, and thanks for your flexibility as well.
56 00:10:37.500 ⇒ 00:10:38.720 Emma Morgan: No worries, thank you so much.
57 00:10:38.720 ⇒ 00:10:40.590 Kaela Gallagher: Alrighty. Thanks. Bye.
58 00:10:40.590 ⇒ 00:10:41.150 Emma Morgan: Bye.