AI Engineering: Under the Hood

AI Engineering: Under the Hood How are modern AI systems actually built, evaluated, and scaled? This meetup is designed for AI Engineers, Software Engineers, ML Engineers, Data Engineers, MLOps/LLMOps Engineers, and developers working with or exploring AI Engineering. The session will go beyond AI demos and explore the engineering layers behind modern AI applications, from models, context, tools, and agents to data, infrastructure, evaluation, reliability, and scale. Agenda 1. Welcome & Introduction to Indian Data Club: 10 min A brief introduction to Indian Data Club, the Bengaluru community, and the focus of the meetup. 2. Icebreaker: 10 min A short networking activity to connect with fellow AI, data, software, and engineering professionals. 3. Speaker Session: 40 min Abhik Sarkar: Director of Machine Learning @ Cloudastructure, Inc. Building AI Systems: Models, Context, Tools & Agents A practical deep dive into how modern AI applications are designed and assembled. What we'll cover: Model selection and LLMs Context engineering RAG and retrieval Connecting models with data and tools Tool calling and agentic workflows AI application architecture Building reliable AI systems AI Engineering Quiz Think you understand AI engineering? Put your knowledge to the test. Join a fun quiz covering AI, LLMs, RAG, AI Agents, and real-world AI systems. 4. Introduction of the Ecosystem Partner Paytm 5. Tea Break Refreshments and informal networking. 6. Panel Discussion: 40 min Engineering the AI Stack: Data, Infrastructure & Evaluation A technical discussion on the engineering challenges behind taking AI systems from prototypes to reliable production applications. 8. Closing Remarks & Open Networking: 30–40 min Closing remarks followed by open networking with speakers, panelists, founders, engineers, and other members of the Bengaluru technology community. What You'll Take Away By the end of the meetup, you'll have a clearer understanding of: How modern AI applications are engineered What sits beyond the LLM How models, context, tools, agents, and data work together The infrastructure required to run AI systems How AI systems are evaluated and monitored The trade-offs between cost, latency, reliability, and performance What changes when moving from an AI prototype to production For any collaboration or partnership, connect with us Name: Sahib LinkedIn: https://www.linkedin.com/in/sahib-musharraf/ Email: [contact removed] [number removed] Book a meeting: https://shorturl.at/ncbqs If you’d like to speak at our events, please fill out this form: Link - Become a Speaker - Inspire India's Data Community – Fill out form Note: Submissions will be reviewed for current or future sessions.