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In this episode, we talked with Ranjitha Kulkarni, a machine learning engineer with a rich career spanning Microsoft, Dropbox, and now NeuBird AI. Ranjitha shares her journey into ML and NLP, her work building recommendation systems, early AI agents, and cutting-edge LLM-powered products. She offers insights into designing reliable AI systems in the new era of generative AI and agents, and how context engineering and dynamic planning shape the future of AI products.TIMECODES00:00 Career journey and early curiosity04:25 Speech recognition at Microsoft05:52 Recommendation systems and early agents at Dropbox07:44 Joining NewBird AI12:01 Defining agents and LLM orchestration16:11 Agent planning strategies18:23 Agent implementation approaches22:50 Context engineering essentials30:27 RAG evolution in agent systems37:39 RAG vs agent use cases40:30 Dynamic planning in AI assistants43:00 AI productivity tools at Dropbox46:00 Evaluating AI agents53:20 Reliable tool usage challenges58:17 Future of agents in engineering Connect with Ranjitha- Linkedin - https://www.linkedin.com/in/ranjitha-gurunath-kulkarniConnect 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/

Ranjitha has shipped AI features across multiple generations of NLP: from speech recognition and online evaluation at Microsoft to LLMs, RAG, and agents in production at Dropbox Dash, and now agentic systems at NeuBird.ai. In this conversation, she shares the concrete practices that make assistants useful, trustworthy, and maintainable in real products. ​ ​We plan to cover:

• ​How early work in speech systems still shapes today’s LLMs and agents
• ​What it takes to turn an LLM demo into a dependable product
• ​Where RAG and agents shine and where they fall short
• ​Skills engineers need today to succeed with applied NLP and agents

​ ​About the speaker

​Ranjitha Gurunath Kulkarni is a Staff Machine Learning Engineer at NeuBird.ai. Previously, she built LLM- and agent-powered product capabilities at Dropbox Dash and worked on speech recognition, language modeling, online metrics, and assistant evaluation at Microsoft. Her publications span voice query reformulation and automatic online evaluation of intelligent assistants, and her patents include automated closed captioning using temporal data and hyperarticulation detection. Ranjitha holds a master’s from Carnegie Mellon University (Language Technologies Institute). ​

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