Builders by Proxify

Proxify
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This is Builders, the podcast where we discuss the ups and downs of building great tech products with the people behind innovative tech products and services.

  1. 1d ago

    The hidden security risks of AI agents, with Ali Leylani | Proxify Talks

    I agents are becoming more capable and more autonomous. But as they gain access to tools, memory, and company data, they also introduce new security risks. In this Proxify Talk, Ali Leylani, Co-founder & Chief AI Officer at Echo Alpha and Chairman of Stockholm AI, explains how AI agent systems work, where they’re vulnerable, and what engineering teams should think about before deploying them at scale. Drawing on his background in AI, cybersecurity, and defense technology, Ali explores the emerging attack surface created by autonomous and multi-agent systems. In this episode, you’ll learn: • How AI agents combine language models, memory, and external tools to perform complex tasks• Why multi-agent systems introduce new security challenges• How prompt injection, jailbreaks, and other attack techniques target AI systems• Why a single compromised agent or data source can affect an entire workflow• How malicious instructions can be hidden inside images and other multimodal inputs • What organizations should consider before giving AI agents greater autonomy. Whether you’re building AI products, leading an engineering team, or exploring agentic workflows, this talk offers a practical look at one of the biggest challenges facing AI adoption today. #AISecurity #ArtificialIntelligence #CyberSecurity #AIAgents #ProxifyTalks

  2. Jul 15

    Why AI adoption fails with Louise Vanerell & Carl Carlheim-Gyllensköld | Proxify Talks

    AI implementation is easy. AI adoption is hard.In this episode, Louise Vanerell and Carl Carlheim-Gyllensköld explore why so many AI initiatives fail to create lasting organizational value despite strong technology investments. The conversation focuses on a critical but often overlooked element of successful AI transformation: the human layer.Together, they discuss how organizations can move beyond technical deployment and drive real behavioral change through effective communication, psychological safety, change management, and internal champions.Whether you’re leading an AI transformation, building an AI strategy, or helping teams adapt to new technologies, this episode offers practical insights for turning AI adoption into measurable business outcomes.In this episode: Why AI adoption often fails in organizationsThe importance of engineering the human layerBuilding trust and psychological safety around AIEffective communication and change management strategiesThe role of champions in technology diffusionDriving behavioral change across teamsMoving from AI usage to organizational valuePractical lessons for leaders navigating AI transformationIf you enjoyed this conversation, subscribe for more discussions on AI strategy, organizational transformation, leadership, and the future of work.#AIAdoption #ArtificialIntelligence #ChangeManagement #AIStrategy #DigitalTransformation #Leadership #FutureOfWork #Innovation #BusinessTransformation #AI

  3. Jul 8 ·  Bonus

    Closing the AI Gap, with Atlan AI’s Rocío Bachmaier | Proxify Talks

    How do companies move beyond AI pilots and actually scale AI across their organization?In this keynote from Proxify HQ, AI strategist and transformation expert Rocío Bachmaier shares practical lessons from helping organizations adopt, implement, and scale AI successfully. Drawing from real-world experience working with companies across industries, she explores the most common mistakes that prevent AI initiatives from delivering meaningful ROI, and what forward-thinking organizations are doing differently.You’ll learn:• The 3 biggest mistakes companies make when scaling AI• Why leadership involvement is critical for AI transformation• How to identify high-impact AI workflows• The difference between AI experimentation and AI adoption• Why AI governance shouldn’t be an afterthought• How AI-native companies are redesigning workflows• What agentic AI means for the future of work• The concept of compound learning in AI systems• How organizations can balance human expertise and AI capabilities• Why AI literacy and training are becoming essential business skillsWhether you’re a founder, technology leader, product manager, developer, or business executive, this keynote offers actionable insights for building AI strategies that create lasting value.This keynote is part of a new series from Proxify, where we share conversations, talks, and expert insights from our headquarters with the wider technology community. Our goal is simple: foster curiosity, share knowledge, and help professionals navigate the future of technology together. Subscribe for more keynotes, expert discussions, the Builders podcast, and insights on AI, software development, leadership, and the future of work.About Proxify:Proxify connects businesses with the world’s top remote software developers, helping companies scale engineering teams quickly and effectively.Learn more: https://proxify.io

  4. Jul 1

    How Slack actually uses AI at work

    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

    How Slack actually uses AI at work

  5. Jun 23

    The end of traditional companies? How AI Is reshaping leadership, hiring & work

    AI, organizational transformation, leadership, hiring, AI native companies, the future of work, organizational design, and AI adoption are changing how businesses operate. In this episode of Builders, Armin Catovic, Director of Data & AI at Funnel, shares a fascinating perspective on how AI is fundamentally transforming organizations—from leadership structures and hiring practices to decision-making, workflows, and team design. We dive into the critical difference between AI-enabled and AI-native companies, why many organizations are underestimating the scale of change ahead, and how AI could reshape competition across entire industries. Armin also explores the future of talent, the evolving role of managers, and why smaller, more agile teams may become the new standard in the AI era. If you're a founder, executive, manager, or technology leader trying to understand what AI means for the future of work, this conversation is packed with practical insights and forward-looking ideas. Topics covered :• AI-enabled vs. AI-native organizations • Organizational transformation through AI • Leadership and decision-making in the AI era • The future of hiring and talent development • AI-driven workflows and collaboration • Attention economics and competitive advantage • Smaller, more effective teams • Operationalizing AI across the business • The future of work and organizational design #AI #FutureOfWork #Leadership #ArtificialIntelligence #AINative #AIAdoption #OrganizationalTransformation #Hiring #Management #BuildersPodcast Chapters (00:00) Why AI Is Forcing Companies to Rethink Everything (01:12) Armin Catovic's Journey into Data, AI & Leadership (03:02) How AI Is Changing Organizations Faster Than Expected (04:42) AI-Enabled vs. AI-Native Companies: The Critical Difference (06:49) Why Traditional Organizational Structures May Not Survive AI (09:07) AI's Growing Role in Workflows, Decisions & Execution (12:17) Addressing Fear, Uncertainty & Workforce Concerns Around AI (16:21) The Surprising Relationship Between AI and Software Demand (18:13) Winning the Battle for Attention in the AI Era (20:53) How AI Is Reshaping Competition Across Industries (22:02) The Future of Talent and the Rise of Junior Inversion (24:39) Developing Talent in an AI-Driven Workplace (25:06) Why Investing in Future Talent Matters More Than Ever(27:09) Essential Skills for Success in the Age of AI (30:22) How Collaboration Is Evolving Across Modern Tech Teams (32:49) Leadership in the Age of AI: What Changes and What Doesn't (36:03) Why Smaller, More Agile Teams Are Winning (37:44) Moving AI from Experiments to Real Business Impact (43:31) Becoming an AI-Native Company: Practical Steps Forward (45:44) The Biggest AI Surprises Still Ahead

    The end of traditional companies? How AI Is reshaping leadership, hiring & work

  6. Jun 16

    Why most AI projects fail in production (and how Amazon solves it)

    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

    Why most AI projects fail in production (and how Amazon solves it)

  7. Jun 3

    Why most Software Engineers are preparing for the future wrong

    Leadership, engineering teams, AI in software development, responsible tech, career growth, and the future of work. How do great engineering leaders build high-performing teams in the age of AI? In this episode of Builders, Bosch’s Kamyar Gilak shares practical insights on leadership, team building, AI tools, software engineering, responsible tech, sustainability, and staying relevant in a rapidly changing industry. We explore how AI is transforming software development, hiring, code reviews, and career growth, while discussing what skills engineers and leaders need to thrive in the future of work. Kamyar also shares lessons from startups and large organizations, strategies for building learning-driven cultures, and why responsible AI and sustainable team practices matter more than ever. Whether you’re a software engineer, engineering manager, tech leader, founder, or someone navigating the impact of AI on your career, this conversation offers actionable advice for building resilient teams and staying ahead of industry shifts. Subscribe for more conversations with technology leaders, founders, and builders shaping the future. #Leadership #AI #SoftwareEngineering #EngineeringManagement #FutureOfWork #ResponsibleAI #CareerGrowth #TeamBuilding #Bosch #TechPodcast #ArtificialIntelligence #FutureOfWork #Developers #Programming #SoftwareDeveloper #TechCareers #StartupLife #Sweden #Germany #Netherlands #NorthAmerica #UK Chapters (00:00) Meet Kamyar Gilak: Leadership, AI & Engineering (02:19) The Leadership Framework: Trust, Clarity & Ownership (06:01) Connecting Talent, Culture & Business Success (08:16) Creating a Culture of Continuous Learning (11:44) How AI Is Reshaping Software Development (19:19) Startup Lessons from Large-Scale Organizations (23:32) The Hiring Challenge: AI’s Growing Influence (26:20) Why AI Is a Developer’s Tool, Not a Replacement (29:10) How AI Is Changing Candidate Evaluation (30:59) The Future of Software Engineering in an AI World (37:39) Protecting Team Focus in a High-Change Environment (43:59) The Most Exciting Advances in Engineering & AI (45:25) The Biggest Shift Happening in Software Development

    Why most Software Engineers are preparing for the future wrong

  8. May 27

    This data science mistake is killing AI projects

    Data science, AI, spam detection, fraud prevention, MLOps, and machine learning teams are reshaping how product companies build trust at scale. In this episode of Builders, Liniker Seixas, Senior Staff Data Scientist and Team Lead at  @truecaller , explains how data science teams can move beyond experiments and build models that actually work in production.Why do so many companies fail to turn data science into business impact, and what does Truecaller do differently?Liniker shares:- How to build practical data science teams that ship real products- Why hiring “unicorn data scientists” is usually the wrong move- How data engineers, MLOps engineers, and product owners support model success- Why vanity metrics like F1 scores and accuracy are not enough- How Truecaller adapts models in a fast-moving spam and fraud environment- Why user feedback is essential for improving spam and fraud detection- How to hire data scientists for curiosity, adaptability, and learning speed- What senior data science hires bring to early-stage and scaling teams- How to build long-term technical strategy without betting everything on today’s AI trendsIf you’re building data science teams, scaling machine learning products, fighting fraud and spam, or trying to connect AI work to real business outcomes, this episode delivers practical lessons from one of the most demanding product environments in tech. 🎧 Subscribe to Builders for more conversations with leaders shaping the future of AI, data science, engineering, and product innovation.#DataScience #AI #MachineLearning #MLOps #FraudDetection #SpamDetection #Truecaller #DataEngineering #ProductLeadership #BuildersPodcastChapters(00:00) How Truecaller Builds Data Science Teams That Ship(01:21) Liniker Seixas’ Journey Into Data Science Leadership(03:35) Why Companies Get Data Science Teams Wrong(04:04) The Magic Wand Fallacy in Data Science Hiring(05:54) Why Data Scientists Shouldn’t Own Everything Alone(07:36) Why Data Science Needs Engineering Support to Scale(08:08) What a Well-Balanced Data Science Team Looks Like(10:09) How Truecaller Keeps AI Models Fresh Against Spam and Fraud(10:51) Why Fast Delivery Beats Eight-Month AI Projects(12:38) What Separates Successful Data Products From Failed Ones(13:22) Why Business Impact Matters More Than Perfect Models(14:28) How to Keep Data Science Anchored to Product Outcomes(16:11) How Truecaller Measures Success Through User Feedback(17:18) Why Guardrail Metrics Matter in Data Science Experiments(18:28) How Truecaller Reframed Spam Detection Around User Behavior(20:38) Building ML Models in a Cat-and-Mouse Fraud Environment(22:16) Why Model Drift and Continuous Learning Matter(24:07) How to Hire Data Scientists for Curiosity and Learning Speed(26:35) Internal Mobility and Growth Inside Data Science Teams(28:28) Why Adaptability Beats the Perfect CV in AI Hiring(30:00) How AI Is Changing Technical Skill Assessment(30:25) Why Data Scientists Must Stay Relevant(31:46) The Role of Senior Data Scientists in Scaling Teams(33:19) Building a Five-Year Vision for Data Science Teams(35:45) How to Prioritize Ideas Across a Long-Term Roadmap

    This data science mistake is killing AI projects
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This is Builders, the podcast where we discuss the ups and downs of building great tech products with the people behind innovative tech products and services.

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  • Creator
    Proxify
  • Years Active
    2024 - 2026
  • Episodes
    78
  • Rating
    Clean
  • Copyright
    © Proxify
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    Builders by Proxify