You will get an AI infrastructure assessment with a clear action plan
Project details
AI systems often fail for operational reasons long before they fail for model reasons. This assessment helps you understand your current AI infrastructure, identify technical and operational risks, and move forward with a practical action plan.
Not sure if you need the full review yet? Start with a focused blind-spot audit first. I can review one architecture view, workflow, or screenshot and tell you what deserves deeper attention.
I review the foundations that matter most: architecture, cloud and Kubernetes readiness, deployment flow, observability, reliability, and platform constraints. You get a structured, decision-ready assessment focused on what deserves attention now, what can wait, and what should be fixed before implementation expands.
Ideal for teams that want to reduce uncertainty and create a clear path toward a stable, scalable AI platform. Deliverables include prioritized findings, recommendations, and a tailored roadmap. Higher tiers add deeper architecture notes, executive framing, and handoff-ready guidance.
Not sure if you need the full review yet? Start with a focused blind-spot audit first. I can review one architecture view, workflow, or screenshot and tell you what deserves deeper attention.
I review the foundations that matter most: architecture, cloud and Kubernetes readiness, deployment flow, observability, reliability, and platform constraints. You get a structured, decision-ready assessment focused on what deserves attention now, what can wait, and what should be fixed before implementation expands.
Ideal for teams that want to reduce uncertainty and create a clear path toward a stable, scalable AI platform. Deliverables include prioritized findings, recommendations, and a tailored roadmap. Higher tiers add deeper architecture notes, executive framing, and handoff-ready guidance.
AI Development Type
Deep Learning, Software MaintenanceAI Tools
Amazon SageMaker, Azure Machine Learning, Google AutoML, Keras, MLflow, NVIDIA AI Platform, Open Neural Network Exchange, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$250
|
Standard
$600
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 3 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
+$100 - $150
Additional Revision
+$60
60-min live walkthrough call
(+ 1 Day)
+$120
Follow-up Q&A and clarification pack
(+ 1 Day)
+$180
Extended architecture notes
(+ 2 Days)
+$250Frequently asked questions
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DS
Danny S.
Jan 23, 2026
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George W.
Jul 9, 2025
Need help with database and python processing
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George W.
May 25, 2025
Need Mysql DBA to advise on how to clean up disk space
Freddy is a HUGE find. It's very rare to find someone as seasoned and talented as Freddy is.
He is professional, cares for his clients, and will gain your REPEAT BUSINESS.
We ending contract as we promised to give him a good review but will be hiring again.
He is professional, cares for his clients, and will gain your REPEAT BUSINESS.
We ending contract as we promised to give him a good review but will be hiring again.
About Freddy Daniel
Senior Platform Engineer | Cloud Cost, Kubernetes, LLMOps
100%
Job Success
Santa Cruz de la Sierra, Bolivia - 5:16 am local time
My work is strongest when a team needs senior judgment before making infrastructure changes, scaling an AI feature, touching production, or spending more on cloud resources.
Typical audits I handle:
- Cloud Cost Leak Check: oversized compute, idle resources, orphaned storage, weak tagging, missing budgets, Kubernetes waste.
- Kubernetes Readiness Review: probes, resource requests/limits, rollout safety, ingress, secrets, scaling, rollback readiness.
- LLMOps Observability Review: latency, token cost visibility, prompt/response logging, RAG blind spots, dashboards, alerting quality.
- MySQL / API / CI-CD Triage: disk pressure, slow queries, FastAPI reliability, Dockerfile risk, pipeline fragility.
I do not need sensitive credentials for an initial audit. A safe first pass usually works from screenshots, exports, logs, YAML files, code snippets, architecture diagrams, or non-sensitive configuration excerpts.
You get a clear report with prioritized findings, risk level, evidence reviewed, practical recommendations, and the safest next step:
- no immediate action,
- a focused implementation sprint,
- or monthly reliability/cost/observability support if the issue is recurring.
Relevant background:
- 20+ years across infrastructure, cloud, telecom platforms, DevOps, automation, databases and production operations.
- Senior IT Cloud Engineer experience with OpenStack, Docker and Kubernetes environments.
- 5.0 Upwork feedback on technical work, including MySQL/database and Python-related support.
If you need a careful senior review before touching production, send me the non-sensitive evidence and I will help you define the safest next step.
Steps for completing your project
After purchasing the project, send requirements so Freddy Daniel can start the project.
Delivery time starts when Freddy Daniel receives requirements from you.
Freddy Daniel works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff and scope alignment
I review your goals, stack, constraints, and current-state inputs so the assessment focuses on the right systems, risks, and outcomes.
Current-state infrastructure review
I analyze architecture, deployment flow, cloud or Kubernetes setup, observability, and operational risks to identify the most relevant gaps.