The patterns I design and ship, from RAG and multi-agent systems to lakehouses and event-driven pipelines. Every one is laid out below: how the pieces fit and how the data flows, end to end.
LLM and agent systems, grounded in real data so they hold up outside the demo.
Grounded answers from your own data
Retrieval-augmented generation done right: the LLM answers from your documents, not its training data, so responses stay accurate and cited. The quality of the retrieval layer is what makes or breaks it.
Specialized agents, coordinated
For tasks too big for a single prompt, a planner breaks the goal into steps, specialist agents handle each with their own tools, and a verification pass checks the result before it ships. Coordination and guardrails matter more than any single model.
Lakehouses, warehouses, and pipelines built to be reliable, governed, and cheap to run.
Layered, auditable, trustworthy
The lakehouse pattern I reach for most: raw data lands untouched, gets cleaned and conformed in stages, and only trusted data reaches the serving layer. Every row is traceable back to its source.
Continuous ELT, in-warehouse
Snowflake doing the heavy lifting: Snowpipe ingests continuously, Streams and Tasks transform in place, and a data-quality gate stops bad data before it reaches the marts. Version-controlled and audit-ready.
Event-driven, near real-time
Ingestion with no human in the loop: a file or event triggers the pipeline the moment it lands, flows through Databricks into Delta, and refreshes reporting in minutes instead of hours.
Metadata-driven and self-healing
An event-driven AWS pipeline where new data triggers processing immediately, a governed lake keeps each zone clean, and metadata-driven Step Functions handle dependencies, retries, and alerts so failures recover themselves.
Hiring for a senior Data/AI role, or need a data platform that actually holds up in production? Let's talk.
or email me directly at muhammaduzairkhan329@gmail.com