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Data Engineering Summit 2026 conference with professionals networking.

MAY. 14-15 2026
Hotel Radisson Blu, ORR /
Bangalore

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Abhishek Kumar

Manager, AI Research at AIM

Abhishek Kumar, with nearly a decade of experience in AI and data science, is a self-taught coder and applied AI expert known for building intelligent systems across domains—spanning predictive modeling, NLP, generative AI, and agentic systems—with a strong focus on impact and scalability.

All Sessions by Abhishek Kumar

  • Day 1 | Hall 3 - Closed Door Workshops


  • 13:40  - 15:40
    Your pipelines work — until they don’t. And when they break at 2 AM, nobody knows why. This workshop fixes that. You’ll learn how to bring real engineering discipline to data — CI/CD, automated testing, version control, and observability applied to pipelines the way software teams have done it for years. We’ll get hands-on with orchestration frameworks like Airflow and Dagster, build data quality checks that catch issues before your stakeholders do, and implement lineage tracking so you always know what went wrong and where. We’ll also connect the dots to LLMOps and ML pipelines, because reliable AI starts with reliable data. Bring your Python or SQL chops. Leave with pipelines that don’t page you at midnight.

  • Day 2 | Hall 3 - Closed Door Workshops


  • 10:30  - 12:30
    Everyone’s talking about prompts and models. Nobody’s talking about the pipes feeding them. Here’s the truth — your LLM is only as good as the data infrastructure behind it. This workshop puts data engineers where they belong: at the center of the AI stack. You’ll build the pipelines that power retrieval-augmented generation — from ingestion and transformation to embedding generation and vector store delivery, using tools you already know like Airflow and dbt. We’ll tackle the unsexy but essential stuff: dataset versioning, embedding lifecycle management, and keeping knowledge bases fresh so your LLM doesn’t hallucinate last quarter’s answers. Bring your Python or SQL skills and a basic understanding of embeddings. Leave knowing how to build the data backbone that makes AI actually work.