Every so often the industry invents a job title, throws it at LinkedIn, and leaves the rest of us to figure out what it means. This year’s contender: forward deployed engineer. Half the internet insists it’s consulting with better branding. The other half seems convinced it’s the last job standing once AI writes all our code for us. I had opinions. I also had questions.
So for episode 4, I got Tanya Dixit on the podcast, a forward deployed engineer at Google, to sort out which half is right (spoiler: neither, entirely). Yes, also called Tanya. No, we did not plan that but the transcript was a nightmare to edit.
Tanya’s route here was not a tidy straight line. She started out building satellite hardware in embedded systems, back before deep learning was cool enough to have its own hoodie. Then the math from her undergrad started clicking, she did a nano-degree, and somehow that spiralled into a master’s from ANU (university medal, no big deal) and a career that’s touched everything from enterprise AI to bushfire risk modelling. By the time she landed on forward deployed engineering, she’d basically done a full lap of the ML lifecycle. Build it, prove it, deploy it, fix it, repeat.
This is why the conversation doesn’t stay in the shallow end. We get into what her actual week looks like, and the coding-to-customer-calls ratio is not what most people guess. If you assume “forward deployed” is code for “technical enough to be in the room but not enough to be at the keyboard”... you’re in for a surprise.
Funnily enough, if this episode leaves you wanting to try the job rather than just hear about it, Tanya’s got you covered there too. On 19th September, she’s co-instructing a live workshop called Forward Deployed Engineering: From AI Demo to Production alongside Keith Bourne (FDE at Tribe AI, author of Unlocking Data with Generative AI and RAG).
It’s not a webinar where you nod along and take notes. You’re handed a realistic 90-day AI-agent deployment for a regulated customer, and you have to scope it, define the success metrics, design the security controls, and then defend the whole plan in a CISO hot seat, which is exactly as fun as it sounds.
You’ll walk away with an actual skills gap analysis, a career action plan, and a much clearer sense of whether this job is for you or just looks good from the outside.
We also get properly into the thing everyone eyeing this career path wants to know: where does an FDE’s job stop and the product team’s job start? Tanya draws that line with far more precision than I expected going in, and her answer for why companies are suddenly hiring for this role like it’s going out of fashion made a few things click for me. Turns out standard SaaS playbooks and AI products are not playing the same game at all.
Then there’s the bit I haven’t stopped thinking about since we recorded. As models get better and coding gets, in her words, increasingly commoditised, does that mean the industry just needs fewer FDEs? She doesn’t reach for the easy optimist answer, and she doesn’t reach for the easy doom answer either. What she gives instead is a proper mental model for which parts of this job survive and which don’t... and yes, we did end up in recursive self-improvement territory, because apparently no 2026 tech conversation is complete without it.
If you're building AI products, working anywhere near enterprise customers, or just trying to work out where the humans still fit in this whole picture, do yourself a favour and watch the full thing rather than skimming for quotes.
The full episode is live now with audio and video. Go watch it, and if Tanya’s take here makes you want more, she’s finally starting to write on Substack too.
And if Tanya’s Forward Deployed Engineering (FDE) Workshop: From AI Demo to Production is still sitting in an open tab somewhere on your browser...
That’s it for this one. We’ll pick up the conversation next week.
Until then, keep building.
Tanya D’cruz
Editor-in-Chief
