You will get a production-readiness audit of your LLM agent or RAG system
Project details
Your agent or RAG prototype works in the demo and wobbles in production. Hallucinated tool calls. Retrieval that misses the one document that mattered. Token spend nobody can explain. No audit trail when something goes wrong.
I audit LLM systems the way someone who carries the pager audits them.
For two years I've been the sole AI architect for a Fortune 500 operator — multi-agent systems across 17+ environments, hybrid-retrieval RAG over 34,369 documents, and a tool layer invoking 40+ production microservices with per-tool authorization. Under that: 15 years of Java and Spring Boot in enterprise production, and 100+ P1 incidents resolved.
You get a written findings report with every issue ranked by what it costs you, a cost and latency profile of your pipeline, a guardrail and failure-mode review, and a live readout call.
If your system is in good shape, I'll tell you that and say where to spend your next engineering month instead. If it isn't, you'll know exactly what to fix first and why.
For teams past the prototype, heading for real users, security review, or a compliance conversation — especially if your AI has to live inside an existing backend rather than beside it.
I audit LLM systems the way someone who carries the pager audits them.
For two years I've been the sole AI architect for a Fortune 500 operator — multi-agent systems across 17+ environments, hybrid-retrieval RAG over 34,369 documents, and a tool layer invoking 40+ production microservices with per-tool authorization. Under that: 15 years of Java and Spring Boot in enterprise production, and 100+ P1 incidents resolved.
You get a written findings report with every issue ranked by what it costs you, a cost and latency profile of your pipeline, a guardrail and failure-mode review, and a live readout call.
If your system is in good shape, I'll tell you that and say where to spend your next engineering month instead. If it isn't, you'll know exactly what to fix first and why.
For teams past the prototype, heading for real users, security review, or a compliance conversation — especially if your AI has to live inside an existing backend rather than beside it.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AIOps, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging FaceAI Models
BERT, ChatGPT, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$950
|
Standard
$1,900
|
Advanced
$3,800
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 21 days |
Number of Revisions | 1 | 1 | 2 |
AI Model Integration | - | - | |
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | ||
Model Deployment | - | - | - |
Model Documentation | |||
Model Monitoring | - | ||
Model Testing & Optimization | - | ||
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | |||
Pre-Training | - | - | - |
Prompt Engineering | - | ||
Setup File | - | - | - |
Source Code | - | - | - |
Frequently asked questions
28 reviews
(28)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
RS
Robert S.
Oct 9, 2023
Senior Java Engineer
Solid Java engineer!
BB
Bassel B.
May 5, 2023
Senior/expert Java developer needed for APIs development
Good developer
RW
Rebecca W.
Apr 4, 2023
Python Developer
Thank you for all your hard work in making this project a success.
RW
Rebecca W.
Dec 4, 2022
Python Developer
WA
Walter A.
Oct 13, 2022
Experienced Java Developer
These reviews can sometimes be vague, so let me be absolutely clear: Moshiour is an exceptionally talented backend developer. He rose to a leadership level fairly shortly after beginning work with us. The project he inherited was challenging across a number of dimensions including having been developed using an ancient version of Java which he stepped in and became not only responsible for maintaining as a real time production application, but also for adding and adjusting features without rewriting the application. Eventually, the client sunset the project, but Moshiour remained until the end as his expertise was needed. Highly recommended!
About Moshiour
LLM Engineer | Multi-Agent Systems & RAG | Fortune 500 + SaaS Founder
Dhaka, Bangladesh - 9:49 pm local time
Flagship client: Princess Cruises (Carnival Corp, Fortune 500) — sole AI architect across 17+ deployed ship environments.
Platform 1: Multi-agent BDI operations assistant. Custom orchestration written from scratch — no framework wrappers. LLM planning, guardrails, human-in-the-loop approvals, self-hosted Ollama at the ship edge. Fully air-gap compatible.
Platform 2: Fleet-wide agentic assistant. Hybrid-retrieval RAG with 34,369 Confluence pages indexed into ChromaDB. MCP tooling invoking 40+ microservices in real time.
15+ years of Java and Spring Boot across 40+ microservices in enterprise production. 100+ P1 incidents resolved — I know what breaks at scale and why.
Most AI engineers are Python-only and treat your existing backend as a black box.
I architect AI directly into enterprise Java infrastructure — agent tooling, RAG pipelines, and LLM orchestration integrated into the systems you already run.
$400K+ earned on Upwork. Top Rated. 12,755 hours logged.
I also run TechyOwls, my own AI SaaS studio — because I prefer shipping over advising.
SnapForge — screenshot and agentic data extraction API. LangGraph-based. Live with paying customers.
Isolate — AI image processing platform. Background removal, upscaling, virtual try-on, local vision LLMs.
LLM Bridge — WireGuard-tunneled inference between VPS and home GPU. Enterprise-grade inference without cloud lock-in.
Stack: LangGraph, LangChain, MCP, ReAct, BDI multi-agent, ChromaDB, pgvector, self-hosted Ollama, Llama 3.x Vision, Claude, OpenAI. Java, Spring Boot, Python, FastAPI, TypeScript. Docker, Kubernetes, Ansible, Prometheus, Grafana. Oracle, PostgreSQL, Redis, Kafka.
WHAT YOU GET
When you bring me in, you get someone who has already made the architecture decisions you're about to make — at Fortune 500 scale, under real compliance pressure, in production environments where failure costs money.
I embed as technical lead alongside your existing team or execute independently — both at the same standard. System design, agent orchestration, RAG pipeline, security review, stakeholder communication. You focus on the product. I keep the AI from becoming a liability.
WHERE I ADD THE MOST LEVERAGE
Teams past the prototype stage that need production-grade AI — systems that survive security review, scale to real users, and run in regulated or air-gapped environments. Enterprise codebases where AI needs to integrate with existing Java/microservices infrastructure,
not replace it.
Currently accepting a limited number of long-term engagements. Fixed-scope architecture audits also available.
Steps for completing your project
After purchasing the project, send requirements so Moshiour can start the project.
Delivery time starts when Moshiour receives requirements from you.
Moshiour works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff and architecture walkthrough
We go through your system end to end: retrieval, model calls, tools, and where it runs. Your answers decide what gets dug into first.
Retrieval review
Chunking, embeddings, index configuration, and hybrid search. I run your real queries and measure what actually comes back.