I don't build demos. I build systems that go to production.

Most AI engineers stop at the model. I start there.

I'm Taimoor Khan — an AI Engineer with deep expertise in architecting enterprise-grade machine learning pipelines, retrieval-augmented generation (RAG) systems, and deploying LLMs at scale.

My work sits at the intersection of ML research and production engineering — taking models from notebooks to reliable, observable, cost-efficient systems that handle real users and real data.

I believe great AI engineering means obsessing over:

• Reliability — systems that don't break at 3am
• Observability — you can't fix what you can't measure
• Scalability — designed for 10x from day one
• Precision — the right model, the right context, the right output

Currently co-building Stonepath Labs — building TAQ — release control for AI agents.

The open source core of TAQ. When an AI agent fails in production, that failure becomes a replayable regression test that runs before every future deployment. If the same failure returns after a prompt, model, or tool change, the release is blocked.

pip install replayd

github.com/TaimoorKhan10/replayd — Part of TAQ by Stonepath Labs.

Production-grade Retrieval-Augmented Generation at scale

A battle-tested RAG architecture built for enterprise deployments, supporting multi-tenant document ingestion, hybrid dense-sparse retrieval, reranking pipelines, and LLM-agnostic query synthesis. Handles 160K+ document corpora with sub-second P95 latency.

Real-world Use Case → Deployed as core intelligence layer for clinical decision support and enterprise knowledge bases.

End-to-end ML pipeline orchestration platform

An MLOps platform enabling automated model training, versioning, evaluation, and deployment across cloud and on-prem environments. Features experiment tracking, data lineage, model registry, drift detection, and CI/CD integration, reducing time-to-production by 70%.

Real-world Use Case → Enables teams to ship ML models to production in hours, not weeks.

Intelligent document understanding and information extraction

A modular NLP system for extracting structured intelligence from unstructured documents at scale — combining entity recognition, relation extraction, document classification, and semantic clustering. Powers automated reporting workflows across high-stakes domains.

Real-world Use Case → Processes clinical notes, legal documents, and financial reports — extracting structured facts with 94%+ accuracy.

Unified hub for fine-tuned and production-ready ML models

A curated collection of production-optimized models spanning computer vision, NLP, and multimodal tasks — each with benchmarks, deployment configs, quantization support, and inference endpoints. Designed for teams who need reliable models without the research overhead.

Real-world Use Case → Accelerates AI adoption in enterprise settings by providing plug-and-play, production-ready model pipelines.

Stonepath Labs is building TAQ — release control for AI agents. When an AI agent fails in production, that failure should become a replayable regression test before the next deployment. replayd is the open source core: pip install replayd. stonepathlab.net