The freedom to make your own choices drives both results and job satisfaction
- Reading time
As an AI specialist, you can work just about anywhere, from tech giants to start-ups.
GenAI Specialist and lead engineer Raghunath Nair joined Rabobank for a reason: freedom. “The freedom we get to make our own choices drives both results and job satisfaction.”
Before joining Rabobank three and a half years ago, Raghunath (Raghu) worked as a senior engineer at an investment bank in Switzerland. Why did he move to Rabobank? “I wanted to work at a large, international organization that actually puts AI into production. What I build here has a direct impact on a lot of people, both customers and coworkers.”
Building It Ourselves
For the past six months, Raghu has been the lead engineer in a new innovation team made up of mostly full-stack GenAI Specialists. In this team, every member takes end-to-end ownership of the product. “We don’t just build AI applications; we’re constantly testing new ideas,” he says. “As a financial institution, we’re faced with the added challenge of strict regulations, including data privacy legislation. By building the core elements of our tools ourselves, we know exactly how everything works and can make processes transparent to regulators.”
As a team, we decide which ideas to pursue and how to develop them."
From Idea to Application
Despite the regulatory framework, Raghu and his team have a lot of freedom in their work—and the lead engineer creates that same space for them. “If we spot an interesting use case, we first test it in a sandbox, a secure test environment. That way, we make sure an innovation doesn’t negatively impact the rest of the organization. We also run hackathons and workshops to explore ideas quickly. As a team, we decide which ideas to pursue and how to develop them.”
Built to Scale
Raghu’s team explores the potential of new ideas with a proof of concept: a simple, initial version to quickly test whether something works and adds value for the user. “If it works and proves secure and scalable, we build on it and turn it into a production-ready solution that can be used across the bank. If an idea doesn’t work, we document it. Even if we don’t use it, that knowledge is still valuable,” says Raghu.
Getting Coworkers on Board
Raghu’s previous team developed a Q&A chatbot for customers. “It’s designed to base its answers on internal data sources, with the goal of giving customers the most reliable answers possible. We also limit the impact of hallucinations by giving the chatbot clear guardrails, setting boundaries for what it can and can’t do.”
Raghu’s current team is building a wide range of AI agents for coworkers. “Four are already live and are helping engineers write code. It’s also up to my team to encourage coworkers to embrace these tools by showing them what they’re capable of. One way we do that is through workshops, for example. We also collect feedback through surveys, so that we can keep improving the agents.”
Rapid Growth
Like other teams, Raghu’s team works with targets and short cycles. “This technology is moving so fast that we often pivot or alter course. We moved from two-week sprints to one-week sprints. At the same time, we make sure there’s space for innovation. We decide how to spend our time: Do we keep building solutions that are moving toward production, or do we explore new ideas? That freedom to make our own choices drives both results and job satisfaction.”
Raghu is fascinated by how quickly AI is transforming the organization. “We’re building agents for the entire bank. They’re not designed for a single squad, but intended to deliver value across the tribes, with the ambition to scale bank-wide over time. That’s why our team has grown so fast. Six months ago, we started with three people. Now we’re a team of ten, and we already need more. The foundations are in place, but we’re only just getting started with scaling.”
