Engineering in the age of AI: Evolving teams, roles and the SDLC

AI in high-stakes environments: Lessons from healthcare

Taking AI from a promising pilot into a live system is where most of the hard problems begin – especially in environments where failure has real consequences. In this episode, Yasin speaks with Raheel Zubairi, founder of Pixelence AI, about what changes when AI stops being an experiment and becomes part of the system. Drawing on Raheel’s experience building AI inside healthcare, the conversation explores the practical realities that rarely show up in demos: regulatory friction, data quality, legacy infrastructure, governance, and the assumptions that break once systems go live. They discuss why so many AI initiatives stall before production, how judgement and validation matter more than speed, and where deliberate restraint is often the most responsible technical decision. While grounded in healthcare, the lessons apply to any mission-critical environment where systems must earn trust, operate under constraints, and perform reliably day after day. If you’re a CTO or technology leader wrestling with how to operationalise AI without compromising safety, accountability, or long-term value, this episode is for you.

Engineering in the age of AI: Evolving teams, roles and the SDLC

Artificial intelligence is already writing code, testing features, and showing up in sprint meetings – but what does that mean for how we build and lead software teams? In this episode, Yasin sits down with engineering leader Ryan Greenhall to unpack the realities of working alongside AI. Drawing on his experience scaling teams through crises and hypergrowth, Ryan shares how AI is reshaping roles, responsibilities, and how the best teams are using AI to amplify strong fundamentals, not replace them. Ryan also unpacks what an AI-augmented SDLC might look like – smaller teams, faster feedback loops and engineers acting as ‘tech leads’ to AI agents. If you’re leading or working in the software industry and wondering how to prepare your teams for the age of AI, this conversation is for you.

Merging code and culture: The technical realities of mergers and acquisitions

When two companies join forces, the headlines focus on valuation and strategy, but under the surface, there’s another story playing out in codebases, infrastructure and teams. In this episode, I talk to Yann Cluchey about what really happens on the technical side of mergers and acquisitions: from uncovering hidden risks during due diligence to the complex work of integrating systems, architectures, and cultures post-deal. Drawing on years of experience guiding technology teams through M&A transitions, Yann shares candid insights on what to look for before you buy, how to assess engineering maturity, and why the hardest part of any merger isn’t the tech; it’s the people who build and maintain it. If you’re an engineering or product leader facing an acquisition, or simply want to understand the unseen technical challenges behind the business headlines, this conversation is for you.

Scaling software systems: Building speed, structure and sustainability in the AI era

Scaling a software system isn’t just about writing more code – it’s about balancing people, processes, and platforms in ways that can survive rapid growth. In this episode, we talk with Gemma Whitehouse, a seasoned CTO and advisor to VC- and PE-backed startups, about the realities of scaling products and teams when everything is moving fast. Drawing on two decades in tech, Gemma shares candid lessons from guiding startups through the messy middle of growth: when technical debt starts to bite, when processes creak under pressure, and when culture makes the difference between resilience and collapse. We also explore how AI is reshaping the challenges of scale; speeding up some problems while magnifying others. If you’re building or leading software in a scaling environment, and wondering how to grow without breaking what already works, this conversation is for you.