Can “Vibe Coding” Ever Replace Deep Engineering Expertise? — Santosh Prasad, FirstMeridian

Santosh Prasad’s journey to the head of engineering at one of India’s largest staffing firms began with a headset and a midnight shift. Answering emergency calls for failing systems gave him a profound, ground-truth understanding of architecture. One that no textbook could provide. Today, at FirstMeridian Business Services, he leads the technical engine behind a workforce of 180,000+ spanning manufacturing, retail, and logistics. Santosh believes that while engineering is the vehicle, clean data is the heart. He argues that in a world obsessed with the fancy stuff of AI, the most effective leaders are those who stay grounded in the fundamentals and actively seek peer exchange to navigate the complexities of scale.

How do “2 a.m. Phone Calls” shape an Engineering Leader?

Santosh reflects on his early career in tech support and how it influenced his approach to high-level architecture.

“I actually started in tech support. I spent my early days answering phone calls at 2 a.m. for customers whose systems had gone down. That experience gave me profound insight into how systems work and how they fail in production. It taught me how to manage emergencies and shaped my thinking regarding system architecture. Later, I had to revive an abandoned project with zero documentation, which taught me how to build from the ground up to be market-ready. Future leaders should start with these basics. If you understand the fundamentals of the user experience at its most critical failure points, you will be much more effective at leading a global organisation.”

Why is the Blue-Collar sector a “Greenfield” for AI?

Moving beyond white-collar staffing solutions, Santosh identifies the massive gap in technology for rural and industrial workers.

“Most existing staffing solutions are designed for white-collar workers. However, a substantial part of our industry serves blue-collar workers, which presents a massive ‘greenfield’ opportunity. In a country like India, blue-collar workers are often scattered across rural areas where advanced technology is scarce. Smart systems can help us reach, hire, and support them where they are. AI can chip in to fill these gaps, but leaders must distinguish what makes sense from what is just ‘fancy stuff.’ If AI boosts productivity and reach for this demographic, it is worth the investment.”

How can organisations separate signal from noise in the data race?

Santosh argues that rapid development often leads to a loss of focus on data structure and integrity.

“The biggest challenge for organisations is separating the signal from the noise. To make data truly useful, it must be clean, properly structured, and aligned with business goals. Many companies struggle because rapid development often leads to a loss of focus on data structure. If you switch context too frequently, you hamper the clarity of your data. Stability in your set of goals is what ultimately provides clarity to your technical infrastructure. Engineering is the vehicle, but clean data is the heart of workforce solution management.”

What is the danger of “Vibe Coding” in a production environment?

While AI has accelerated the prototyping phase, Santosh warns that it can lead to massive cost inefficiencies if not fine-tuned by experts.

“Two years ago, prototyping took a long time. Today, you can prototype a product in a week. However, there is a difference between ‘vibe coding’ a prototype and building a production-grade system. You can use AI to build a prototype quickly, but you need deep engineering expertise to fine-tune the model for cost optimisation and stability. You cannot afford to burn thousands of dollars on tokens in a production environment due to poor planning. Leaders must strategise better to ensure that rapid innovation translates into sustainable software deliverables.”

How do you lead an Engineering team as a “Guardrail” rather than a Dictator?

Santosh discusses the importance of a psychologically safe environment for fostering ownership and innovation.

“Whenever my team brings forward new ideas, I prefer a democratised understanding of the system. I act as a guardrail rather than a top-down dictator. I mentor them to understand what works and what doesn’t, creating a space where they feel listened to and valued. I believe a psychologically safe environment is essential for innovation. When engineers receive constructive feedback in a safe space, they find better ways to refine their solutions. It is about enforcing a culture where people take ownership and responsibility for the systems they ship.”

Join the Conversation: The Ortus Club’s Executive Network

Across Santosh’s insights on blue-collar greenfields, context switching, and the transition from prototyping to production, one pattern is clear: these challenges aren’t solved in isolation. They require a peer-level perspective and the kind of high-trust dialogue that transcends technical noise.

His vision of the “Leader as Guardrail” reflects a broader reality: today’s engineering heads cannot rely on internal speed alone to stay efficient. The most effective executives, especially those managing massive workforces in BFSI and manufacturing, actively seek out peer dialogue as a strategic necessity to separate the signal from the noise.

At The Ortus Club, we host curated executive roundtables that bring together senior leaders facing these exact challenges. Step away from the “vibe coding” hype and engage in the kind of open, high-value conversations that ensure your rapid innovation translates into sustainable business outcomes.

Frequently Asked Questions

Q: What is the difference between “Vibe Coding” and Production Engineering?

A: “Vibe coding” refers to using AI to quickly generate code based on loose prompts to see if an idea works. Production engineering involves fine-tuning that code for security, scalability, and cost efficiency so it can run reliably at scale.

Q: Why is blue-collar staffing considered a “Greenfield” opportunity?

A: Because the majority of HR technology has been historically optimised for office-based, white-collar environments. There is significantly less infrastructure dedicated to hiring and managing rural or industrial workforces.

Q: How does Context Switching harm data integrity?

A: When teams pivot between shifting requirements too frequently, the underlying data structures become messy and inconsistent, making it difficult to generate accurate, long-term business insights.

Q: What is a psychologically safe engineering environment?

A: It is a culture where team members feel comfortable taking risks, admitting mistakes, and providing honest feedback without fear of punishment, leading to higher ownership and better software quality.

Q: How can AI optimise costs in workforce management?

A: By automating the matching of rural workers to job sites and streamlining payroll for thousands of employees, AI can reduce the manual overhead and operational costs associated with large-scale staffing.