AWS Landing Zone - A Technical Guide to Enterprise Cloud Foundations
When an organization starts using AWS, the first few teams can usually create resources manually:
Designing resilient AWS cloud platforms and taking generative AI systems from prototype to production.
I help startups and enterprises architect, migrate, and optimize mission-critical AWS workloads — and build production-ready generative AI, RAG, and agentic systems with a rigorous focus on reliability, security, and cost efficiency.
Reference architectures for secure, scalable, and resilient systems aligned with the AWS Well-Architected Framework, including the Generative AI Lens.
Production-grade AI applications on Amazon Bedrock and SageMaker: RAG over private knowledge, prompt and evaluation pipelines, guardrails, and cost-aware model selection.
Tool-using agents and event-driven workflows that automate document processing, support operations, and internal back-office tasks with human-in-the-loop controls.
Ingestion, vector stores, feature and embedding pipelines, model deployment, monitoring, and retraining workflows that keep AI systems accurate over time.
Phased migration strategies, re-platforming, and modernization of legacy workloads to cloud-native, serverless, and AI-ready AWS services.
CI/CD automation, infrastructure as code, observability, operational runbooks, and incident-ready systems for production cloud and AI workloads.
Foundational infrastructure, compute, and networking for production services.
Foundational and fine-tuned model infrastructure on managed AWS AI stacks.
High-throughput transactional, analytical, and semantic search data stores.
Declarative automation, delivery pipelines, and deep observability.
Delivered staged AWS migrations with rollback-safe releases, cutover planning, and zero unplanned downtime for mission-critical systems.
Turned fragile AI prototypes into governed, monitored services with grounded retrieval, latency optimization, and automated evaluation checkpoints.
Implemented CI/CD and immutable infrastructure automation that eliminated manual deployment toil and increased release velocity.
Reduced compute and LLM token expenditures through architectural right-sizing, caching, model tiered routing, and usage-aware scaling.
When an organization starts using AWS, the first few teams can usually create resources manually:
Many AI teams monitor latency and token usage but still don't know why their production AI system is failing.
A model that ranks #1 on a public benchmark may be the wrong model for your production workload.
Vector search is powerful because it retrieves documents based on semantic meaning, not just exact words.
A production RAG system is not one component. It is a multi-stage retrieval and generation pipeline, and every stage can introduce failure.
production systems need to handle everything that happens around that LLM
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Industry Experience: Healthcare systems, B2B SaaS, data platforms, and high-compliance enterprise workloads.
Available for strategic architecture reviews, Well-Architected audits, and hands-on consulting.