You will get AWS SageMaker Cost + Security Audit
Project details
Most SageMaker environments have two problems
running simultaneously — they are overspending
and they are exposed. This engagement addresses
both in a single five-day audit.
What you get:
• Live dashboard — Cost, Security, and Executive
Overview tabs
• Exportable PDF findings report with severity
rankings across cost and security dimensions
• IAM policy review, endpoint exposure assessment,
and encryption audit
• Prioritized remediation roadmap
• Executive summary
• 60-minute findings walkthrough call
Delivered in 5 business days. Multi-account
coverage available on Standard and Advanced tiers.
running simultaneously — they are overspending
and they are exposed. This engagement addresses
both in a single five-day audit.
What you get:
• Live dashboard — Cost, Security, and Executive
Overview tabs
• Exportable PDF findings report with severity
rankings across cost and security dimensions
• IAM policy review, endpoint exposure assessment,
and encryption audit
• Prioritized remediation roadmap
• Executive summary
• 60-minute findings walkthrough call
Delivered in 5 business days. Multi-account
coverage available on Standard and Advanced tiers.
AI Development Type
Deep Learning, Model Tuning, Software MaintenanceAI Tools
Amazon SageMaker, Azure Machine Learning, MLflowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$4,000
|
Standard
$6,000
|
Advanced
$8,000
|
|---|---|---|---|
| Delivery Time | 5 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 2 |
AI Model Integration | - | - | - |
Detailed Code Comments | - | - | - |
Knowledge Graph | - | - | - |
Model Documentation | |||
Ontology | - | - | - |
Source Code | - | - | - |
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$500 - $750
Findings Presentation
(+ 1 Day)
+$250
Second AWS Account
(+ 2 Days)
+$500
Remediation support
(+ 2 Days)
+$750Frequently asked questions
About Jose
AI Engineer | RAG, LLM Apps, Agents, AWS & MLOps
Las Vegas, United States - 8:01 am local time
I’ve worked on enterprise engineering teams delivering production systems through Agile/Scrum environments, owning work across architecture, development, infrastructure, CI/CD, cloud platforms, security, reliability, and cost optimization.
Today, I focus on building production-ready AI applications and platforms, including RAG systems, LLM-powered applications, AI APIs, and cloud-native AI infrastructure.
My approach is shaped by production engineering experience: AI systems should not only work in a demo — they need to be secure, observable, scalable, reliable, and economically sustainable.
What I can help you with:
• RAG and enterprise knowledge applications
• LLM-powered applications and AI APIs
• Agentic AI workflows and tool integrations
• Python backend development with FastAPI and Django
• AWS AI infrastructure using Bedrock, SageMaker, ECS, EKS, and Lambda
• PostgreSQL, vector search, and document retrieval pipelines
• MLOps, CI/CD, Docker, Terraform, monitoring, and deployment automation
• AI performance, latency, and cost optimization
• Cloud and AI security using IAM, VPC networking, encryption, and least-privilege design
Recent work includes:
• Designed and implemented enterprise RAG and knowledge-retrieval workflows
• Built and deployed scalable Python-based AI backends
• Implemented cloud infrastructure and deployment automation on AWS and Azure
• Reduced infrastructure and inference costs through autoscaling, right-sizing, and architecture improvements
• Improved reliability, observability, and security of production cloud systems
Tech stack:
Python, FastAPI, Django, React, TypeScript, PostgreSQL, RAG, vector search, LLM APIs, AWS Bedrock, SageMaker, Lambda, ECS, EKS, Azure, Terraform, Docker, GitHub Actions
If you’re building an AI product, RAG platform, document-processing workflow, or LLM-powered SaaS application and need someone who understands both the AI layer and the infrastructure required to run it in production, I can help.
I’m also happy to review an existing architecture or implementation and identify practical improvements around reliability, security, performance, and cost.
Steps for completing your project
After purchasing the project, send requirements so Jose can start the project.
Delivery time starts when Jose receives requirements from you.
Jose works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff (Day 1)
45-minute call to confirm scope across accounts, priorities, and access requirements. You grant read-only IAM access using our setup guide — takes under 10 minutes per account to configure.
Environment Inventory (Day 1–2)
We inventory all SageMaker resources across your account(s) — endpoints, training jobs, notebook instances, pipelines, and supporting compute. CloudWatch utilization and CloudTrail data pulled for the preceding 30 days.