You will get a production RAG readiness audit and prioritized remediation roadmap
Top Rated
Project details
Use this focused audit when your RAG system exists but retrieval quality, answer reliability, evaluation, guardrails, latency, observability, access boundaries, or operating cost remain unclear. I will review the supplied architecture, configuration, sanitized examples, and failure evidence, then deliver a current-state architecture summary, retrieval and ingestion risk assessment, evaluation and guardrail gap analysis, latency and cost-risk review, and a prioritized remediation roadmap. This project is an assessment only. It does not include implementation, production deployment, model training or fine-tuning, formal security or compliance certification, or review of unrelated application components. Production access is not required by default.
AI Algorithms
Large Language ModelAI Applications
Natural Language UnderstandingAI Development Language
PythonAI Models
GPT-4What's included $750
These options are included with the project scope.
$750
- Delivery Time 5 days
- Number of Revisions 1
Frequently asked questions
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AB
Ashley B.
Feb 16, 2026
AWS Infrastructure Design and Implementation with Terraform
We needed a production-ready AWS foundation and Rahul delivered a well-structured Terraform architecture.
The modules were reusable, networking was properly segmented, and security defaults were correctly implemented. The infrastructure is now version-controlled, reproducible, and easy to operate.
He also documented the system clearly, making onboarding new engineers much easier.
Excellent work — this is how infrastructure should be built from day one.
The modules were reusable, networking was properly segmented, and security defaults were correctly implemented. The infrastructure is now version-controlled, reproducible, and easy to operate.
He also documented the system clearly, making onboarding new engineers much easier.
Excellent work — this is how infrastructure should be built from day one.
SR
Sona R.
Feb 16, 2026
Fix AWS Lambda Cold Starts and Optimize DynamoDB Performance
Rahul implemented our AWS infrastructure with Terraform and turned a messy setup into a reliable, repeatable platform.
Clean structure, safe rollout, and great communication. Highly recommended.
Clean structure, safe rollout, and great communication. Highly recommended.
MB
Maryam B.
Feb 16, 2026
AWS cost optimisation audit and implementation for saad platform
Rahul conducted a full AWS cost optimization audit for our SaaS platform and the results were immediate.
He didn’t just suggest generic savings — he analyzed our architecture, traffic patterns, and billing behaviour and identified several hidden inefficiencies we were completely unaware of.
Within the first implementation phase we reduced our AWS bill significantly while improving system stability and response times. The changes were safe, well-explained, and executed without service disruption.
What stood out most was his engineering approach — every recommendation had a clear reasoning, risk analysis, and rollback plan. This was not a “cost cutter”, this was proper cloud architecture work.
If you run production workloads on AWS and your bill feels unpredictable, Rahul is the person you want looking at it.
He didn’t just suggest generic savings — he analyzed our architecture, traffic patterns, and billing behaviour and identified several hidden inefficiencies we were completely unaware of.
Within the first implementation phase we reduced our AWS bill significantly while improving system stability and response times. The changes were safe, well-explained, and executed without service disruption.
What stood out most was his engineering approach — every recommendation had a clear reasoning, risk analysis, and rollback plan. This was not a “cost cutter”, this was proper cloud architecture work.
If you run production workloads on AWS and your bill feels unpredictable, Rahul is the person you want looking at it.
AH
Ali H.
Jan 23, 2026
Cloud & DevOps Engineer for Small Teams (AWS/GCP/Azure)
Rahul was a strong addition to our team.
He understood our multi-cloud setup across AWS, GCP, and Azure and helped us tighten both architecture and delivery workflows. His work around automation and CI/CD was practical and reliable, and he collaborated well with developers to smooth out deployment and operational issues.
What stood out was his focus on stability and long-term maintainability, not quick fixes. Communication was clear, and he took ownership of problems until they were fully resolved. Highly recommend
He understood our multi-cloud setup across AWS, GCP, and Azure and helped us tighten both architecture and delivery workflows. His work around automation and CI/CD was practical and reliable, and he collaborated well with developers to smooth out deployment and operational issues.
What stood out was his focus on stability and long-term maintainability, not quick fixes. Communication was clear, and he took ownership of problems until they were fully resolved. Highly recommend
RE
Remy E.
Jan 23, 2026
AI/ML Task Automation Specialist Needed
Rahul did exactly what we needed. He took the time to understand our existing ML workflow, identified where improvements would be most effective, and designed a clean, reliable pipeline. The solution was amazing it used an LLM where appropriate and included proper structure, logging, and error handling. The results improved both efficiency and consistency, the handoff was clear enough for us to maintain or extend independently, and communication was smooth with on-time delivery. Excellent work we’d gladly work with him again.
About Rahul
AI Solution Architect | AI Agents, RAG, Claude, AWS & Automation
100%
Job Success
Surat, India - 6:36 am local time
I help SaaS and enterprise teams design, build, and rescue production AI systems on AWS.
Expert-Vetted Top 1% AI Consultant on Upwork
- 100% Job Success
- $80K+ earned
- 2,700+ hours delivered
- 41 Upwork engagements
- 9+ years of production engineering experience
- Three AWS Professional certifications in Generative AI, Solutions Architecture, and DevOps
✓ CLIENTS USUALLY CONTACT ME WHEN
- An AI agent works in a demo but fails with real users, tools, permissions, or business data
- A RAG system retrieves the wrong information, produces weak citations, or becomes unreliable at scale
- An AI proof of concept needs to become a secure and maintainable production product
- Claude, OpenAI, or Amazon Bedrock must be connected to real business workflows
- AWS infrastructure has reliability, security, scalability, or cost problems
- A development team needs senior technical ownership rather than another task-based developer
- An existing implementation needs architecture review, debugging, or production rescue
→ WHAT I BUILD
PRODUCTION AI AGENTS
- AI agents and agentic workflows
- Claude, OpenAI, and Amazon Bedrock integrations
- Tool calling, MCP servers, and external system integrations
- Human approval and escalation workflows
- Agent memory, state management, and context handling
- Evaluations, guardrails, tracing, and failure recovery
- Multi-agent orchestration where it is genuinely justified
PRODUCTION RAG SYSTEMS
- Document ingestion and processing pipelines
- Vector search and hybrid retrieval
- Metadata filtering, reranking, and access control
- Reliable citations and source attribution
- Retrieval and response evaluations
- Enterprise knowledge assistants
- Multi-tenant RAG architecture
- OpenSearch and vector database integrations
AWS ARCHITECTURE AND PLATFORM ENGINEERING
- Serverless and event-driven systems
- AWS Lambda, API Gateway, DynamoDB, SQS, EventBridge, and Step Functions
- Terraform, AWS CDK, CloudFormation, and infrastructure automation
- IAM, tenant isolation, security boundaries, and least-privilege access
- Monitoring, observability, incident recovery, and architecture reviews
- AWS cost optimization, performance improvement, and cloud migrations
★ SELECTED PRODUCTION EXPERIENCE
- Worked on a high-volume ordering platform processing millions of transactions per month with strict uptime, performance, and recovery requirements
- Delivered a 686-hour AWS engagement involving Cognito, Lambda, SQS, backend services, and production infrastructure, followed by a five-star client review
- Improved production systems across SaaS, healthcare, fintech, cybersecurity, and streaming
- Owned delivery across architecture, critical implementation, deployment, security, observability, and engineering handoff
I work hands-on across Python, Node.js, TypeScript, APIs, distributed systems, cloud infrastructure, and modern AI frameworks such as LangGraph and LangChain.
→ HOW I WORK
I do not begin by selecting an AI framework.
I begin with:
- The business workflow
- The users and permission boundaries
- The available data
- The expected result
- The failure modes
- The security requirements
- The operating cost
- The team's ability to maintain the system
From there, I design the smallest architecture that can safely achieve the required outcome.
I will also tell you when:
- An AI agent should be a deterministic workflow
- RAG is unnecessary for the use case
- A managed service is creating avoidable cost or lock-in
- The proposed architecture is too complex
- A feature is likely to waste more money than it creates in value
- A prototype is not yet ready for production use
Avoiding the wrong architecture often saves more time and money than writing additional code later.
You can expect clear communication, visible progress, documented decisions, honest risk analysis, and production-focused validation. I stay involved through production readiness and engineering handoff, not only prototype delivery.
I am a strong fit when AI must interact with real users, business data, external tools, and production infrastructure.
Send me your architecture, requirements, repository context, or production problem.
I will review the context, identify the highest-risk area, and recommend the most practical starting point.
Steps for completing your project
After purchasing the project, send requirements so Rahul can start the project.
Delivery time starts when Rahul receives requirements from you.
Rahul works on your project following the steps below.
Revisions may occur after the delivery date.
Review system evidence and prioritize findings
I review the architecture, retrieval flow, evaluation approach, guardrails, observability, latency, and cost evidence, then deliver the prioritized remediation report.