Builders by Proxify

AI is changing technical roles faster than ever. But does that mean customer-facing engineers are becoming obsolete?In this episode of Builders, Lee Haynes sits down with Liliana Lindberg, Lead Solutions Engineer at Slack, who has also worked at Google and startups throughout her career. They discuss what solutions engineers actually do, why technical expertise still matters in the age of AI, and why blindly trusting AI can create bigger problems than it solves.Liliana also shares her unconventional journey into tech, why she thought programming wasn’t for her, and how she built a successful career without following the traditional management path.In this episode, you’ll learn:• What a Solutions Engineer actually does• Why AI won’t replace customer-facing technical roles• How Slack uses AI to improve productivity• The biggest mistakes people make when using AI• Why technical knowledge is still essential in the AI era• Startup vs. big tech, what each environment teaches you• Why becoming a manager isn’t the only path to career growth• The skills every modern Solutions Engineer needs• How curiosity became Liliana’s biggest career advantage• Practical advice for anyone building a career in technologyWhether you’re a software engineer, solutions engineer, engineering leader, founder, or simply curious about how AI is reshaping work, this conversation offers practical insights you can apply today.Subscribe for more conversations with engineering leaders, technology executives, and innovators building the future of software.Chapters00:00 Introduction01:09 From psychology to engineering03:13 Thinking she chose the wrong career04:37 What a Solutions Engineer actually does07:00 The biggest misconceptions about the role09:23 The skills that matter most12:41 Startups vs. big tech18:31 Why she chose the individual contributor path22:46 How AI is changing technical work26:32 Why you shouldn’t trust AI blindly28:54 Why Slack changed how she works31:18 AI agents and the future of collaboration34:57 Avoiding AI tool overload36:36 The best workflow she’s seen in Slack37:46 What she looks for when hiring41:58 Building trust in remote teams42:57 Career advice she wishes she’d received sooner45:15 What’s next for Slack46:42 Advice for aspiring Solutions Engineers

Data Engineering, Machine Learning, AI, Data Trust, AI Governance, and ML Ops are transforming how companies scale intelligent systems.In this episode of Builders, Deepak Yadav, Engineering Leader at Amazon with 19+ years of experience in data engineering, analytics, and AI/ML, shares what it really takes to move machine learning from proof of concept to production.We explore the hidden challenges behind operationalizing AI, building trusted data platforms, implementing AI governance at scale, and creating systems that organizations can rely on. Deepak also discusses how AI is reshaping data engineering, the future of automation, and emerging trends like decision intelligence, synthetic data, and self-healing systems.Whether you're a tech executive, AI leader, VP of Engineering, CTO, Data Engineer, or ML engineer, this conversation offers practical insights into scaling AI responsibly and effectively.Topics covered:• Data Engineering and AI at scale• Machine Learning production challenges• Data Trust and AI Governance• ML Ops best practices• AI adoption in enterprises• Hiring and scaling data teams• Startups vs. enterprises in AI• Decision intelligence and synthetic data• Future AI trends and automation#DataEngineering #MachineLearning #AI #MLOps #DataTrust #AIGovernance #DataPlatforms #DataScience #AITech #BuildersPodcast