Building reliable AI products in the era of Gen AI and Agents
Details
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](http://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](http://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).
**Join our slack: https://datatalks.club/slack.html**