Building a Career in AI From Real Estate to AI Engineering - Gustaf Gyllensporre

Building a Career in AI From Real Estate to AI Engineering - Gustaf Gyllensporre

In this talk, Gustaf Gyllensporre, Senior AI Engineer and PropTech Founder, shares his unconventional career journey from selling Miami real estate to shipping production AI systems. We explore the tactical steps for breaking into the tech space as a self taught developer and how to successfully bypass traditional industry gatekeepers.Links:-  @PropTechFounder  [https://studio.youtube.com/channel/UC_tr3idZY_ZVq_ZTRBdu-6w] - https://youtu.be/leXRiJ5TuQo?si=ymK03qKVEC7hAt9N- https://x.com/gostak_ddYou will learn about:- The strategic approach to crafting an AI engineering resume that actually gets noticed by hiring managers.- Why building another generic RAG chatbot might be hurting your portfolio and the specific high impact projects you should build instead.- The massive difference between interviewing for AI roles at dynamic startups versus traditional big tech companies.- How to leverage open source contributions to prove your technical mastery without a computer science degree.- The surprisingly simple networking tactics and developer ambassador programs that can unlock exclusive job opportunities.- Actionable ways to improve the critical social and communication skills that most developers completely ignore.TIMECODES:00:00 AI Engineering Field Guide05:01 Self Taught AI Engineer Pivot09:39 CPython Open Source Contributions13:49 Tech YouTube Channel Growth18:26 AI Engineer Resume Optimization22:44 AI Engineering Portfolio Projects29:23 Startup vs Big Tech Interviews33:47 Open Source AI Project Ideas38:55 Building Deep Research AI Agents42:52 Landing Your First AI Job46:48 Tech Networking Strategies51:16 Technical Project Demo Videos54:53 Self Taught Developer Mistakes58:53 Soft Skills for Software EngineersConnect with Gustaf- Linkedin - https://www.linkedin.com/in/gustaf-g/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

How to Build AI that actually Ships in Production - Aleksandr Kim

In this talk, Aleksandr Kim, Senior Data Scientist at Intuit, shares his expertise in building AI-powered features in production from fine-tuning BERT models in cyber security to engineering scalable data verification platforms. We explore the reality of moving beyond messy research code to build observable, cost-effective AI agents and automated pipelines.You’ll learn about:- Translating traditional machine learning metrics into actionable business outcomes- Validating large language model behavior through robust evaluation and alignment techniques- Pivoting from a generic chatbot project to high-value Slack automation workflows- Structuring outputs and guided reasoning layers to eliminate trivial AI summaries- Defining the overlapping skills between AI engineers, data scientists, and full-stack software engineers- Implementing multi-LLM routing logic and token caching to minimize enterprise API expenses- Identifying critical data infrastructure bottlenecks to determine when to pivot or drop an AI pilotTIMECODES:00:00 AI Engineering Production and Scalability06:12 Intuit Ecosystem and QuickBooks Products12:17 Aligning ML Metrics with Business Outcomes18:52 AI Engineers Conducting Customer Interviews25:13 Structured Output and Guided Reasoning31:13 Defining AI Engineering vs Software Engineering37:20 Cost Optimization and Multi LLM Routing43:26 UI Trends and Token Management in Industry49:33 Future Career Trends in AI Engineering55:46 Data Infrastructure Bottlenecks and ML FailuresThis session is designed for mid-to-senior level Data Scientists, Machine Learning Engineers, and Software Engineers who want to develop a highly practical, production-first approach to generative AI. It is especially useful for technology leads focused on reducing token overhead and building self-correcting agentic systems.Connect with Aleksandr- Website - https://alexkimds.github.io/- Linkedin - https://www.linkedin.com/in/aleksandrkim/

AI Adoption in Enterprise Beyond Writing Code - Ivan Bilan

In this talk, Ivan, Senior Engineering Manager at Personio, shares his deep expertise in the data and software space from his early days building traditional NLP systems and massive ETL pipelines to his current leadership role in Identity and Access Management (IAM). We explore the rapid evolution of Generative AI, the reality of managing AI agents in production, and the emerging field of context engineering to optimize developer workflows.You’ll learn about:- The buy vs. build dilemma for AI infrastructure and local LLMs.- How AI agents are shifting workloads and evolving code reviews.- Why AI is currently better at fixing tech debt than building from scratch.- Measuring the ROI of AI integration using DORA metrics and cycle times.- Strategies to manage vendor lock-in and minimize AI provider dependency.- Using "context engineering" and specification-driven development to maximize LLM quality.- Why hiring junior engineers is still essential and how AI accelerates their onboarding.TIMECODES:00:00 Career Journey in Data Science and NLP07:37 Industry Adoption of Generative AI and Agents11:45 Buy vs Build Dilemma for AI Infrastructure15:46 AI Capability Limits in Fixing Tech Debt19:32 Developer Workloads and AI Code Contributions24:49 Experimentation with Open Source AI Agent Architectures30:06 Measuring ROI and Business Value of AI Integration35:10 Tracking AI Impact Using DORA Metrics39:51 Impact of AI Code Generation on CI/CD System Reliability43:00 Best Practices for Team AI Tool Adoption48:20 Managing Vendor Lock-In Risks with AI Providers51:27 Importance of Hiring Junior Software Engineers56:28 Accelerated Junior Developer Onboarding with AI Assistants01:00:12 Specification-Driven Development and Context EngineeringThis talk is perfect for software engineers, engineering managers, and technical leaders looking to practically integrate AI tools into their teams without sacrificing code quality or system reliability. It is especially valuable for tech professionals navigating the complexities of AI adoption, CI/CD pipeline management, and organizational scaling in the GenAI era.Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/ Connect with Ivan:- LinkedIn - https://www.linkedin.com/in/ivan-bilan/ - Twitter - https://x.com/demiourgosua - Github - https://github.com/ivan-bilan - Website - https://github.com/ivan-bilan

Applied AI 2026 Berlin Conference Interview

The conference highlighted a critical shift in the technology and engineering ecosystem, moving away from passive implementations toward autonomous AI systems, collaborative communities, and robust engineering guardrails. Discussions centered on the practical architecture required to scale AI safely, the evolution of modern developer tools, and the importance of cross-border technical collaboration. Ultimately, the insights underscored that the future of technology relies on blending rigorous infrastructure with human-centric ecosystem growth. Florian Hönicke an expert in engineering infrastructure, explored the operational shifting of cloud services and the challenges of secure temporary access provisioning. He detailed strategies for managing transient credentials for large groups and autonomous agents using automated serverless functions without exposing long-lived access keys. His central thesis argues that true engineering rigor requires deterministic, self-expiring security layers at the container level. Stella Buhalis, a technical community and developer relations leader, addressed the human dynamics fueling open-source ecosystems and community-driven adoption. She emphasized that long-term project viability stems from structured developer onboarding and lower cognitive barriers rather than pure marketing outreach. Her key insight is that building trusted technical communities acts as the ultimate feedback loop for improving developer experience and software reliability. Błażej Nowakowski, a backend systems architect, focused on database migration paradigms and the optimization of high-dimensional vector search at the network edge. He analyzed real-world infrastructure friction points, specifically isolating SQLite database lock conflicts and remote data sync latencies on serverless architectures. He noted that decoupling persistent remote backends from the core runtime is crucial for maintaining low-latency, multi-cloud application performance. Alena Astrakhantseva, a talent strategy and engineering education specialist, outlined the rapid evolution of technical training as the industry shifts from traditional development to autonomous AI flows. She analyzed how continuous testing, real-time monitoring, and structured evaluation frameworks must become core competencies for new developers. Her notable perspective highlights that the next wave of technical talent must be hired for systemic engineering rigor over simple syntax mastery. Zhen Ming Ng (Babypro), an open-source library maintainer and developer, demonstrated automation workflows for package deployment and baseline library compliance. He focused on minimizing framework overhead by substituting heavy, resource-intensive dependencies with lightweight tokenizers and compact client drivers. His core perspective is that library design must prioritize minimalism to remain functional across edge-native runtime environments. Connect with speakers: Florian HönickeCloud Infrastructure & DevOps Engineer Specialisthttps://www.linkedin.com/in/florian-h%C3%B6nicke-b902b6aa [https://www.google.com/search?q=https://www.linkedin.com/in/florian-h%25C3%25B6nicke-b902b6aa] Stella BuhalisDeveloper Relations & Technical Community Leadhttps://www.linkedin.com/in/stella-buhalis [https://www.linkedin.com/in/stella-buhalis] Błażej NowakowskiBackend Systems Architect & Database Engineerhttps://www.linkedin.com/in/b%C5%82a%C5%BCej-nowakowski-096716168/ [https://www.linkedin.com/in/b%C5%82a%C5%BCej-nowakowski-096716168/] Alena AstrakhantsevaTechnical Talent Strategist & Engineering Educatorhttps://www.linkedin.com/in/alenaastra/ [https://www.linkedin.com/in/alenaastra/] Zhen Ming Ng (Babypro)Open Source Software Maintainer & Core Developerhttps://www.linkedin.com/in/ming91/ [https://www.linkedin.com/in/ming91/]

From GenAI Pilots to Production - Nikita Kozodoi

In this talk, Nikita, Senior Applied Data Scientist at the AWS Generative AI Innovation Center, shares his expertise in bringing enterprise artificial intelligence out of the sandbox—from his early days optimizing traditional machine learning models like gradient boosting to deploying advanced production-grade GenAI pipelines. We explore what it really takes to move generative AI systems from pilot prototypes to production environments.Links:- AWS Generative AI Innovation Center: https://aws.amazon.com/ai/generative-ai/innovation-center/You’ll learn about:- Deploying multi-layered defenses independent of backend LLMs.- Evaluating parameter-efficient methods like LoRA and QLoRA for small models.- Balancing long-term domain expertise with real-time documentation retrieval.- Utilizing multi-agent orchestration for search and anomaly explanation.- Setting up robust LLM-as-a-judge frameworks verified by human metrics.- Leveraging Amazon Bedrock components for memory and runtime scalability.TIMECODES:05:52 Shifting from traditional ML to generative AI07:49 Hybrid pipelines blending classical ML and LLMs11:25 Production guardrails and multi-layered system defense16:15 Prompt bypasses, input attacks, and AI red teaming20:49 Newsletter localization and translation with Zalando27:24 Evaluation frameworks and human-in-the-loop metrics33:07 Aligning LLM-as-a-judge with few-shot prompts34:49 Fine-tuning small language models versus prompting41:18 Complementary mechanics of RAG and fine-tuning43:00 Agentic web search tools for anomaly explanation47:01 Automated text generation from real-time sports sensors49:58 AWS project scoping and proof of concept timelines54:58 Interview requirements and career skills for AWS roles57:59 Enterprise architecture patterns and system observability01:00:42 Reusable infrastructure blocks on Amazon BedrockThis session is designed for machine learning engineers, data scientists, and technical product managers looking to architect reliable, production-ready GenAI workflows. It is highly valuable for teams aiming to bridge the gap between experimental AI prototypes and secure enterprise software.Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/ Connect with Nikita- Linkedin - https://www.linkedin.com/in/kozodoi/- Github - https://github.com/kozodoi- Website and blog - https://www.kozodoi.me/