Show Notes
In this episode Revathy Ramalingam, Senior Software Engineer and AI Engineer at a healthcare startup, shares her inspiring personal journey from over nine years in telecom software architecture to successfully transitioning back into the industry after a seven-year career break. We explore the evolution of the AI engineer role, the practical application of RAG pipelines, and the strategic use of AI tools to rebuild a technical career.
You'll learn about: - AI Career Mapping: Using LLMs to design an upskilling roadmap. - Vibe Coding: Leveraging AI tools for rapid prototyping. - RAG Implementation: Building retrieval systems with LangChain. - Interview Strategy: Proving technical skills after a career gap. - Learning in Public: Building a network through community projects.
TIMECODES: 00:00 Why Move to AI? Using ChatGPT to Plan a Career Pivot 11:00 Learning in Public: The Power of Community Support 15:35 Telecom Capstone: Predicting Network Slices with ML 22:15 "Vibe Coding" & Building Prototypes with AI Dev Tools 28:00 The Interview Process: Navigating a 7-Year Career Break 33:45 Practical Interview Tasks: Building a PDF Q&A Assistant 39:40 Career Advice: Clear Plans, AI Mentors, and Hard Work 44:30 Closing Thoughts: Scaling the Learning Ladder
This talk is for developers and career-changers looking for a blueprint to enter the AI engineering space. It is ideal for those interested in RAG, healthcare tech, and practical career resets.
Connect with Revathy - Github - https://github.com/RevathyRamalingam - Linkedin - https://www.linkedin.com/in/revathy-ramalingam/
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/
More from DataTalks.Club
Tejas Kumar joins Alexey Grigorev to discuss building evaluation datasets, calibrating LLM judges against human preferences, and testing agents beyond their final answers.
In this talk, Dewang Sultania, ML Engineer at Netflix and co-author of Designing AI Systems, shares his technical journey from building early inference platforms at Adobe to deploying multimodal AI models at scale. We explore the complete architecture of enterprise AI platforms, focusing on evaluation datasets, tool calling orchestration, and the practical realities of shipping production agents.
In this talk, Suhas Suresha, Senior Machine Learning Engineer at Adobe and co-author of Designing AI Systems, shares his expertise in building large-scale generative AI infrastructure from deploying real-time ECG analysis models at his previous startup, QALY, to architecting enterprise LLM platforms. We explore the massive engineering effort required to take an AI application from a weekend prototype to a scalable production system, and how to build a unified AI platform that eliminates "AI sprawl."
In this talk, Sahil Walia, Senior Technical Architect at Snowflake, shares his extensive data engineering expertise from writing simple SQL queries early in his career to architecting enterprise-scale Apache Iceberg lakehouses today. We explore the evolution of the modern data platform, the mechanics of open lakehouses, and how decoupling storage from compute is transforming data architecture.
In this talk, Philip Christos, Associate at M2VC, shares his expertise in frontier AI investing from his early days as a data analyst to evaluating cutting-edge deep tech startups. We explore how AI agents are reshaping modern work, the realities of venture capital in the LLM era, and why the future of software engineering relies more on high-level systems thinking than writing syntax.You’ll learn about:- How venture capitalists formulate AI investment strategies and evaluate startup pitch decks.- The rapid adoption of AI in software engineering compared to traditional sectors like accounting.- The crucial difference between data progress and algorithmic progress in frontier models.- Why LLMs still struggle with high-level software architecture, business logic, and design "taste."- How AI is accelerating iterations and breakthroughs in classical engineering and scientific research.- What specific deep tech and AI infrastructure ideas are actually attracting VC funding today.TIMECODES:00:00 Venture Capital Career Journey05:14 VC Analyst vs Data Analyst Roles10:13 AI Investment Thesis Strategy16:00 Emotion Analytics and Voice AI Startups22:09 LLM Impact on Venture Capital28:19 Software Engineering AI Adoption34:18 Data vs Algorithmic AI Progress40:15 Classical Engineering AI Acceleration46:12 Software Architecture AI Limitations51:11 Future Software Engineering Careers57:55 Systems Thinking in DevOps1:03:00 Pitching Deep Tech StartupsThis talk is perfect for software engineers, AI founders, and technical product managers who want to understand the evolving tech job market and venture landscape. It provides essential insights for anyone looking to build VC-backed AI startups or future-proof their career as AI coding tools become ubiquitous.Connect with Philip- Linkedin - https://www.linkedin.com/in/philipchristos/- Twitter - https://x.com/phil_christos- Website - https://t.me/stacked_channelConnect 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/