You will get an AI agent architecture sprint with an implementation-ready backlog
Top Rated
Project details
This production-oriented architecture sprint defines an AI-agent workflow before expensive implementation begins. I will map the business workflow and agent boundaries, identify deterministic-versus-agent decision points, design tools, state, approvals, retries, guardrails, observability, and failure handling, and produce an AWS deployment architecture plus prioritized implementation backlog. The project excludes production implementation, UI development, third-party software fees, and fully autonomous workflows without agreed approval and failure boundaries.
AI Algorithms
Large Language ModelAI Applications
Conversational AIAI Development Language
PythonAI Models
GPT-4What's included $1,250
These options are included with the project scope.
$1,250
- Delivery Time 7 days
- Number of Revisions 1
Frequently asked questions
34 reviews
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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 Agent & GenAI Engineer | RAG, Automation, AWS Architecture
100%
Job Success
Surat, India - 3:10 pm local time
I build AI agents, RAG systems, workflow automation, and the AWS infrastructure required to operate them. I work across the full delivery path: clarify the business problem, design the architecture, implement the critical path, connect the system to existing APIs and data, and prepare it for production ownership.
Teams usually bring me in when:
- a RAG system returns inconsistent answers, times out, or lacks evaluation;
- an AI-agent prototype needs dependable tools, state, approvals, and failure handling;
- an AI workflow must integrate with an existing SaaS product or business process;
- AWS infrastructure is difficult to deploy, debug, secure, or control financially; or
- the team needs one senior engineer who can connect AI behavior with production architecture.
My core work includes:
AI agents and automation — tool-calling workflows, structured outputs, human approvals, state management, API integrations, retries, guardrails, and operational failure paths.
Production RAG — document ingestion, retrieval design, prompt and response structure, evaluation plans, access boundaries, latency review, observability, and cost-per-request analysis.
AWS architecture — Amazon Bedrock, Lambda, API Gateway, DynamoDB, S3, Step Functions, EventBridge, SQS, Cognito, ECS, EKS, Terraform, CDK, CloudWatch, and production delivery workflows.
Visible Upwork proof includes 100% Job Success, Top Rated status, $80K+ earned, more than 2,400 hours, and 40 completed jobs. One production Cognito, Lambda, and SQS engagement ran for 686 hours and earned a five-star client review. Other five-star, client-endorsed work includes AWS infrastructure with Terraform, SaaS cost and reliability analysis, a production RAG pipeline fix, multi-cloud delivery improvements, and AI/ML workflow automation.
I will push back when a deterministic workflow is safer than an autonomous agent, when a simpler AWS design will ship faster, or when an AI feature lacks evaluation, rollback, observability, or cost controls.
I communicate architecture decisions directly and document the trade-offs so your engineering team can operate the system after handoff.
To start, send me:
1. Your current stack
2. What you are trying to ship
3. What is failing, risky, or unclear
I will respond with the first production risks I see and the most practical next step.
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.
Design agent architecture and implementation backlog
I review the workflow and constraints, define tools, state, approvals, guardrails, failure paths, and AWS deployment design, then deliver the prioritized backlog.