AI Digital Assistant
An interactive digital twin powered by Amazon Bedrock and AWS Lambda. Ask questions and learn more about my background, experience, and approach to LLM engineering.
BedrockLambdaRAG
LLM Engineering & AI Systems
Building intelligence that performs under pressure.
Custom RAG pipelines, fine-tuned models, and production ML systems on AWS Bedrock and SageMaker. Battle-tested on enterprise data with measurable results.
From Michelin-starred kitchens to Fortune 500 AI systems — I bring the same obsessive precision to language models that I brought to the pass. The details are where it wins or breaks.
About
Whether it's RAG systems that hallucinate under real queries, fine-tuned models that ace benchmarks but drift in production, or Bedrock deployments where nobody measured anything — the same pattern keeps showing up.
I started in technology, then spent years training in Michelin-starred kitchens where precision and flawless execution under pressure weren't optional — they were survival.
That experience taught me that complex systems either work exactly as designed, or they fail at the worst possible moment.
When I returned to tech and dove deep into LLM engineering, I found the same fundamental gap: teams shipping models that impressed in demos and collapsed on real data. The tooling had changed dramatically — Bedrock, SageMaker, vector databases, fine-tuning pipelines — but the discipline around evaluation, monitoring, and production readiness hadn't kept up.
I've built and optimized RAG pipelines for enterprise document corpora, fine-tuned domain-specific models for regulated industries, and architected production ML systems on AWS that teams can actually maintain. My goal is always the same: make moving fast and building reliably the same thing, not competing priorities.
6x
Retrieval Precision Improvement
80%+
LLM Projects Fail in Production
F500
Enterprise Experience
Services
Scalable AI infrastructure. Measurable performance. Production-ready from day one.
01
Retrieval-Augmented Generation (RAG)
- Pipelines that surface the right information at the right time, at scale
- Vector database architecture and embedding optimization
- Hybrid search with semantic and keyword retrieval
- Context window management and chunking strategies
02
Fine-Tuning & Model Adaptation
- Domain-adapted models using LoRA and QLoRA techniques
- Evaluation against real-world benchmarks and edge cases
- Training data curation and quality control
- A/B testing frameworks for model performance
03
AWS Bedrock
- Production LLM deployment and infrastructure
- Prompt engineering and optimization
- Guardrails implementation for safety and compliance
- Cost optimization and usage monitoring
04
SageMaker Pipelines
- End-to-end ML workflows with full observability
- Model monitoring and drift detection
- Automated retraining triggers and deployment
- Feature stores and data versioning
Projects
Selected work demonstrating LLM engineering and production AI systems.
+
More projects coming
Contact
Ready to discuss your project? Get in touch.
Based in
Available Worldwide