The AI Shift in Tech Hiring: Lessons From a Revealing Interview
The Interview That Opened the Curtain
A few days ago, I was interviewing a candidate for a Lead Software Architect role. The task was straightforward: “Design the architecture for a scalable airline booking application.”
For the next 30 minutes, we went back and forth. There were conversations about services, queues, failovers, user roles, and data models. Eventually, the candidate sketched a diagram that was about 50% complete—still missing key components like observability, data privacy, and AI integrations.
I paused and asked:
“What value are you adding in this workflow if a tool like ChatGPT can generate this diagram in 2 minutes?”
The silence in the room said it all.
The Reality: AI Can Now Do the ‘First Draft’ of Most Technical Work
That moment wasn’t about diminishing human creativity—it was a wake-up call. The real question tech leads must ask themselves is:
What can I do that AI can’t? Or better yet, how can I use AI to multiply my impact?
New AI-Driven Skills Technical Leads Need Today
- Prompt Engineering & Instruction Design: You don’t need to write thousands of lines of code. You need to write the right prompt that gets a prototype built in minutes.
- Rapid Prototyping with AI: AI tools allow you to generate data pipelines, web apps, and even UI mockups without touching Figma or Visual Studio.
- AI-Oriented Architecture Thinking: Leads must now design systems that incorporate AI from day one—summarization APIs, LLM agents, smart routing logic—not just CRUD services.
- Evaluation and Tuning of LLMs: Understanding how to choose models (GPT-4, Claude, Gemini), prompt them, fine-tune them, or build wrappers around them is critical.
- Workflow Automation and Agentic Thinking: Instead of orchestrating APIs manually, think in terms of agents that self-operate, self-correct, and learn from feedback.
- AI Governance and Ethical Coding: AI opens new doors, but also new risks. Great leads understand where to draw the line, ensure fairness, and validate outputs.
Real - Word Examples from My CTO Desk
Let me be real. I’m not a full-time coder. But with the right AI mindset and tools, I was able to build and deploy multiple solutions in just days—without a team of engineers behind me.
· Policy Document Comparison Tool:
Used AI to extract, align, and highlight changes across 100+ page policy documents. No manual diffing. Delivered in a day.
· Insurance App Summary Generator:
Scraped insurance websites, distilled business summaries, and auto-tagged risks. One script. One AI agent. Done in hours.
· Legal Signature Detection System:
Built a system to detect digital signatures in thousands of scanned files using OCR and LLM validation logic. Previously a 3-week job. Now 3 hours.
These aren’t edge cases. They’re the new normal. What used to require product managers, analysts, devs, QA, and UAT can now be led—end-to-end—by a well-trained AI-first tech lead.
The 2-Speed Architecture of Modern Development
| Speed-1 | Speed 2 |
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| AI Role: Takes the lead in generating first drafts, automating repetitive work, and enabling quick testing of ideas. | AI Role: Acts as a supporting assistant (e.g., for simulations), but not the decision-maker—humans define, validate, and own output. |
| SDLC: Lightweight, fast, prompt-driven development. | SDLC: Structured SDLC with design reviews, version control, and audit trails. |
| Complaince and Governannce: Minimal regulatory constraints; faster experimentation is safe. | Complaince and Governannce: Must follow strict data privacy, compliance, and explainability guidelines (HIPAA, GDPR, SOC 2, etc.). |
| Speed: High speed, low risk—ideal for experiments and operational efficiency. | Speed: Slower speed, high criticality—designed for systems that must not fail or misbehave. |
What Technical Leads Need to Learn Now
To thrive in this dual-speed world, leads need to evolve in two directions:
- Master AI Tools:
- Prompt engineering
- Prototyping workflows with agents
- Integrating LLMs for summarization, analysis, routing
- Using open-source and commercial APIs for automation
- Preserve Engineering Discipline:
- Architecting complex, layered platforms
- Securing data and ensuring compliance
- Managing multi-team development at scale
- Building trust into systems through testing and governance
It’s not “AI or traditional.”
It’s “AI AND traditional”—applied where each fits best.
The Future of Tech Leadership
Tomorrow’s leaders won’t just build systems. They’ll orchestrate humans, AI tools, and platforms into a coherent flow. They’ll know when to move fast and when to pause. When to let AI lead, and when to lean on human wisdom.
Final Thought: Lead with Speed—and Sense
If ChatGPT can draw your diagram in 2 minutes, it’s not a threat—it’s your new assistant.
As a tech lead, your real value is now in:
- Knowing what to build
- Choosing the right speed
- Using the right tool (AI or human)
- Ensuring everything aligns with purpose, ethics, and reliability
The future is not one-speed.
It’s dual-lane. And it’s already here.