You will get a production RAG and MLOps architecture audit on Google Cloud
Project details
You will get a practical, production-focused audit of your RAG, GenAI or MLOps architecture on Google Cloud. I will review the current data flow, retrieval design, model integration, deployment path, reliability, security boundaries, cost and operational readiness. The output is a clear set of findings and a prioritized roadmap—not a generic checklist. Depending on the tier, I will also provide a target architecture, deployment and CI/CD guidance, cost/latency recommendations, an implementation backlog and a walkthrough. I bring 7+ years of AI/ML and cloud engineering experience, $20K+ earned on Upwork, and Google Cloud Professional Machine Learning Engineer and Cloud Architect credentials.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging Face, PyTorch, TensorFlowAI Models
GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$300
|
Standard
$750
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 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
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SC
Samuel C.
Sep 17, 2024
GA4 Raw Data Export Setup in BigQuery
Raghu worked very hard for a period. Which was necessary, because he took on the project without truly understanding what was needed. This particular contract was completed, but it has turned out subsequently that we don't have the data needed and this cannot be retrieved. Still, I'd have been inclined to work with Raghu and put this down to a misunderstanding that I'm at least partially to blame for. But, Raghu simply stopped replying to my messages. The time it took to complete the project without Raghu providing any handover of the work completed. I have waited several weeks for a response to Raghu before closing the contract, he has not replied.
I have done a lot of work as a freelancer, I'm extremely reluctant to leave a negative review as I know how damaging they can be. However, as a business owner, I feel a responsibility to provide accurate feedback.
Raghu is a hard worker, and has many skills. But I would advise spending additional time ensuring that Raghu actually understands what he is agreeing to. Particular if the brief is complex or unusual. He treated my project like an experiment, and when it didn't work, he abandoned it, leaving me to pick up the pieces.
Good luck Raghu, I hope this proves to be a useful learning curve for both of us.
I have done a lot of work as a freelancer, I'm extremely reluctant to leave a negative review as I know how damaging they can be. However, as a business owner, I feel a responsibility to provide accurate feedback.
Raghu is a hard worker, and has many skills. But I would advise spending additional time ensuring that Raghu actually understands what he is agreeing to. Particular if the brief is complex or unusual. He treated my project like an experiment, and when it didn't work, he abandoned it, leaving me to pick up the pieces.
Good luck Raghu, I hope this proves to be a useful learning curve for both of us.
CK
Christopher K.
Jul 1, 2024
Transfer data from Google UA to Google Bigquerry
Well done - really responsive and good communication.
MO
Matthew O.
Jan 23, 2024
Google Cloud Platform Billing Explanation
KR
Karthik R.
Dec 12, 2023
Help setup Vertex AI/Jupyter Notebook with Github
OM
Omar M.
Oct 16, 2023
Python code get Data from API to Google BigQuery Table
Will hire again!
About Raghu
Senior AI & Data Engineer | Python, RAG, Snowflake & Databricks
Basoli, India - 3:11 am local time
$20K+ earned across 22 Upwork jobs | 7+ years in AI, machine learning, data and cloud engineering
What I can build and improve:
• Production RAG systems with ingestion, hybrid retrieval, reranking, citations, grounding checks, evaluation and observability
• LangGraph and agentic workflows with persistent state, structured outputs, tool calling, human approval, retries and controlled failure handling
• Secure MCP servers and API integrations with authentication, least-privilege access, validation and audit logging
• ETL/ELT and ML data pipelines using Python, SQL, BigQuery, PySpark, Airflow, Snowflake, Databricks and cloud-managed services
• Machine-learning solutions for forecasting, classification, NLP, computer vision, feature engineering, model serving and monitoring
• Python and FastAPI backends, REST APIs, Docker, CI/CD and Kubernetes
• Production deployment across GCP and AWS using Cloud Run, Vertex AI, BigQuery, Dataflow and Amazon Bedrock
• Operational dashboards and client-facing AI interfaces that expose quality, latency, cost and failure signals
My public portfolio contains tested implementations for RAG evaluation, LangGraph orchestration, secure MCP access, AWS Bedrock deployment and AI operations monitoring. My commercial experience includes retail forecasting, hierarchical ML systems, PySpark feature pipelines, TensorFlow training and inference, semantic search, Cloud SQL, Dataproc and multi-environment delivery.
I start by understanding your business outcome, current architecture, data constraints, failure cases and definition of done. You receive transparent milestones, tested code, deployment guidance, observability and maintainable handover documentation.
Send me your problem, current stack and expected outcome. I will recommend the smallest practical first milestone.
Steps for completing your project
After purchasing the project, send requirements so Raghu can start the project.
Delivery time starts when Raghu receives requirements from you.
Raghu works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery and current-state review
Review the supplied architecture, data flow, deployment setup, constraints and business goals.
Architecture, risk and gap analysis
Assess retrieval, data, model, MLOps, security, reliability, cost and operational readiness.