Matt Walsh

The Great Recalibration is Happening The tech job market isn't broken. It's recalibrating. Everyone sees the layoff headlines (400k+ since 2023). But here's what they're missing: Engineering leaders' confidence in hiring is UP by 12 percentage points year-over-year, and average hiring targets for software engineers have jumped 28% in the US.   This isn't a downturn. It's a strategic pivot. Companies are shedding legacy roles to fund a massive investment in AI and core engineering. The demand for top tech talent has never been higher—it's just become incredibly specific. Question: Hiring managers, are you feeling this shift from volume to value in your hiring? Join our talent community 🦾 🚀 🤖 : www.bluukazi.com 🚀  #TechHiring #Recruiting #AI #FutureOfWork #TalentAcquisition

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A script that crashes on bad input is annoying for 30 seconds. You see the traceback, fix the flag, re-run. A script that silently succeeds with bad input costs hours. I ran a tenant onboarding script that writes 38 SSM parameters. I swapped two flags. The script exited 0, printed its success banner, and wrote every parameter to the wrong path. I didn't find out until External Secrets Operator started failing reconciliation downstream. My debugging path: ESO reconciliation failure → ExternalSecret status → ClusterSecretStore config → IAM permissions → finally running get-parameters-by-path and seeing the params under the wrong prefix. Four wrong hypotheses before reaching the actual cause. The fix was two lines of input validation. A regex on the tenant name format. An enum check on the app name. The script dies immediately on bad input instead of writing 38 parameters to a path nobody will ever read. Exit 0 is a contract. It means "I did what you asked and it worked." A script that exits 0 after writing data to the wrong location has violated that contract in the most expensive way — it told you everything is fine while creating a problem you won't discover until something else breaks. Format validation on inputs that become path components is not defensive programming. It's the minimum bar for a script that claims success.

"How's the market?" - I've been asked this half a dozen times this week alone, from CTOs growing their teams to engineering leaders thinking about their next move. Here's what's actually happening in the product engineering hiring market right now. Companies are hiring. But they're being strategic about it. I'm seeing leaders take time to think about role design before posting jobs. Breaking down what used to be one senior engineer role into specialised positions. Integration engineers. Platform engineers. R&D-focused engineers. They're not rushing. They're investing in getting it right. Why? Because a bad hire costs more than an empty seat. And in a market where there's finally time to be thoughtful, leaders are using it. Hybrid and remote flexibility? Non-negotiable for most people I speak with. But I'm noticing Melbourne and Sydney presence still matters for collaboration and culture, especially in scale-ups. Strong candidates are getting multiple opportunities. Not hundreds. But quality options with companies that have their act together. Clear vision. Good culture. Smart leadership. If you're a talented product engineering professional who's been sitting tight, now's actually a good time to explore. The roles out there are with companies thinking long-term, not panic hiring. And if you're a leader building a team, you're competing for talent that has choices. Your process, your speed, and your candidate experience all matter. What are you seeing in your corner of the market right now?

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Hiring engineers for hard operational problems is different. Most candidates can write clean code. Fewer can sit with a dispatcher at 7am and understand why their shift planning breaks down. Fewer still can go back to the codebase and design something that actually works in production with those constraints. What I look for at Mirage Metrics is not just skill with Python, SQL, or distributed systems. It is a willingness to go into environments that are messy, sometimes chaotic, and keep digging until the real bottleneck shows up. That can mean inventory mismatches hidden in Excel, planning rules written on a whiteboard, or API endpoints that return inconsistent data depending on who queries them. The best engineers I have worked with are the ones who do not flinch at this. They treat debugging a customs declaration pipeline with the same seriousness as debugging a memory leak. They know that solving “boring” problems in data quality or workflow mapping is what unlocks everything else. On the technical side, I test for clarity. Can someone explain the trade-offs between running a model via an API versus hosting it on GPUs without getting lost in jargon. Can they design a schema validator that will still make sense six months later when requirements change. Can they write glue code that is robust, not fragile. On the personal side, I look for stamina. These projects are rarely about building a shiny feature in isolation. They require iteration with operators, late-night debugging of OCR failures, and the patience to integrate into legacy systems that were never designed for AI. If you want to work on AI for logistics, manufacturing, or mining, the question is not only whether you can code. It is whether you can hold your ground in the real world, in front of the people whose work depends on your system. That is the bar.

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