Jev: The AI Model That Doesn't Generate Text
The AI industry has spent the last few years making language models better at generating text.
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.
The AI industry has spent the last few years making language models better at generating text.
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.
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Private, 100% in-browser image tools: compress, resize, crop, convert, upscale, watermark, and passport photo maker.
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.