On December 9, 2024, Shakudo hosted another captivating fireside chat featuring two distinguished industry leaders: Farhan Thawar, current VP and Head of Engineering at Shopify, and Yevgeny Vahlis, the co-founder and CEO of Shakudo. The pair shared their insights on the current state of AI and data, and discussed their visions for the profound impact of AI on the tech industry, specifically on how the future of engineering can be shaped by these advances and what it means for both aspiring professionals looking to get into the industry and leaders looking to scale up amid the rapid pace of technological change. 

One of the central themes of the conversation was the evolving role of engineering in an AI-driven future. Both leaders agreed that engineers are at the heart of AI’s future, but their role is shifting significantly as AI moves from a tool for automation to a driver of broader transformation across industries. 

Setting the Stage: Leveraging LLMs in Engineering and E-Commerce Innovation

During the fireside chat, Farhan shared his experiences leading the engineering team at Shopify, guiding the company’s evolution from a small startup to one of the biggest e-commerce platforms in the world today. The company has experienced massive growth over the years, and the engineering team has played a crucial role in scaling Shopify’s infrastructure to meet the demands of millions of merchants and customers globally.  

Being a tech visionary himself, Farhan emphasized the importance of hands-on experience from the upper management level, highlighting Shopify’s “founder-mode” culture, where leadership stays deeply involved in the day-to-day engineering and product development. This approach has been key to maintaining agility and innovation, even as the company scaled.

Farhan also underscored the transformative role of AI and large language models (LLMs) in modern business operations. Farhan explained how LLMs are incorporated into Shopify’s interview process as they seek engineering talent, helping to assess problem-solving abilities and coding skills more efficiently. As part of their engineering evaluation process, Shopify has been leveraging AI to streamline workflows and enable smarter decision-making, illustrating the growing importance of LLMs in shaping both the tools engineers use and the experiences they build for users. 

Key Takeaways 

Here’s a quick rundown of the highlights from the fireside chat: 

1. The Shift Towards AI-First Innovation

A significant point in the conversation was the shift toward AI-first innovation. As Farhan explained, AI is no longer a novelty or a supplement—it is at the core of innovation across industries. For Shopify, AI is being used to optimize everything from customer interactions to supply chain operations. Engineers are not just building the infrastructure for AI; they are directly contributing to AI-driven business strategies.

This shift means that engineering teams must have a deep understanding of how AI can solve real-world business problems. Rather than just working on isolated features, engineers are increasingly expected to think about the broader AI ecosystem and its impact on customer satisfaction, business agility, and long-term growth. 

2. Grounding Yourself in a Data-Driven Future 

During the chat, Farhan and Yevgeny highlighted the importance of data literacy for leaders in today’s AI-driven world, emphasizing that leaders must remain hands-on with the technical side of their team’s work. While the engineering team focuses on the execution of AI projects, leaders should be able to understand how to code, debug, and evaluate performance, as well as ensure data quality, so they can understand the technical landscape with an in-depth perspective and provide actionable feedback when needed. Leaders who remain grounded in this sense can better navigate challenges when it comes to model scaling, data governance, and ensuring team cohesion as the company scales. 

3. The Future of Engineering 

The tech industry is constantly evolving, and so too is the role of engineering, which has long served as the backbone of development. Farhan and Yevgeny both acknowledged that the role of the engineer is evolving from traditional coding and system design to become more interdisciplinary. Engineers of the future will need to be proficient in AI model development, machine learning algorithms, and data infrastructure, in addition to core software engineering skills. 

Furthermore, engineers should be equipped with the skills to utilize AI and machine learning to optimize their workflow and solve complex challenges more efficiently. Take Shopify, for example—the company doesn’t just encourage its employees to use AI; it actively integrates the use of large language models into its culture. The ability to leverage AI tools like LLMs has even been incorporated into part of the interview process, allowing the company to assess whether a candidate is adaptable to the evolving tech landscape and capable of using AI to enhance their problem-solving capabilities. 

4. The Problem that Shakudo Solves

As a comprehensive operating system, one of the key benefits highlighted by Shakudo is its ability to provide a unified operating system for the entire AI toolchain and data workflows. The platform’s core strength lies in its scalability and flexibility, which empowers businesses to build customized data ecosystems that are efficient, secure, and agile, making it easier for companies to deploy AI models at scale.  

For many organizations, AI deployment is hampered by the complexity of managing data pipelines, ensuring data quality, and scaling models to meet the demands of growing datasets. With over 170 best-of-breed data tools already incorporated into its platform, Shakudo helps businesses streamline their entire data pipeline so that they can focus on building and deploying AI models rather than managing infrastructure. Businesses seamlessly integrate their preferred tools and scale dynamically, empowering them to scale efficiently while maintaining data integrity and security throughout the process.