Dr Raminderpal Singh

Engineering discipline meets AI momentum

At heart, I'm an engineer. I enjoy solving systems problems — understanding how the pieces fit together, where the friction is, and what it takes to make something work reliably at scale. That instinct shapes everything I build.

What excites me about this moment is the raw momentum that AI brings. The speed at which ideas can become working software has fundamentally changed. But speed without discipline produces fragile systems, hallucinated outputs, and technical debt that compounds fast.

I believe the real opportunity is in combining both: the acceleration AI offers with the rigour that engineering demands. That's the thread running through all my work — from teaching scientists to vibe‑code responsibly, to building test‑first workflows that keep AI agents honest, to deploying autonomous research platforms that scientists can actually trust.

Life sciences has an AI adoption problem — and it takes more than technology to fix it

Most pharma and biotech organisations — both drug‑discovery companies and the vendors that serve them — know they need AI. But knowing and doing are very different things.

The gap isn't one capability. It's a jigsaw of interconnected pieces: how teams write and ship software, how computational scientists run in‑silico experiments, how lab data flows through automated workflows, how leadership structures adapt, and whether the culture genuinely supports change or merely talks about it.

Solving any one piece in isolation delivers limited value. The opportunity is in solving them together.

That's why John Conway (Founder & Chief Visioneer, 20/15 Visioneers) and I have partnered to address the full jigsaw — combining AI engineering and in‑silico software with organisational change, culture transformation, and FAIR data strategy. Between us, we cover the technical foundation and the human infrastructure that AI adoption demands.