You will get an AWS cost and reliability review with a prioritized roadmap
Top Rated
Project details
Most AI agents die between the demo and production. They break on real tool calls, real permissions, and real data, or they quietly burn money nobody budgeted. This sprint exists to prevent exactly that.
In 7 days you get a production-grade architecture for your AI agent: what the agent decides versus what stays deterministic, tool boundaries, human approval gates, failure and fallback behavior, AWS deployment design, a cost model, and a prioritized implementation backlog your team (or I) can execute immediately.
Who you are working with: Expert-Vetted (top 1% on Upwork), 100% Job Success across 41 jobs, 3x AWS Professional certified (Generative AI, Solutions Architect, DevOps), 9+ years of production engineering. I build agent systems with LangChain, LangGraph, and MCP on Claude, OpenAI, and Amazon Bedrock.
One honest promise: if an AI agent is the wrong tool for your workflow, the sprint will tell you that too, and design the deterministic alternative instead. You pay for architecture judgment, not for enthusiasm.
Not sure which tier fits? Book my 30-minute architecture review consultation and I will tell you which one you actually need.
In 7 days you get a production-grade architecture for your AI agent: what the agent decides versus what stays deterministic, tool boundaries, human approval gates, failure and fallback behavior, AWS deployment design, a cost model, and a prioritized implementation backlog your team (or I) can execute immediately.
Who you are working with: Expert-Vetted (top 1% on Upwork), 100% Job Success across 41 jobs, 3x AWS Professional certified (Generative AI, Solutions Architect, DevOps), 9+ years of production engineering. I build agent systems with LangChain, LangGraph, and MCP on Claude, OpenAI, and Amazon Bedrock.
One honest promise: if an AI agent is the wrong tool for your workflow, the sprint will tell you that too, and design the deterministic alternative instead. You pay for architecture judgment, not for enthusiasm.
Not sure which tier fits? Book my 30-minute architecture review consultation and I will tell you which one you actually need.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AIOps, Anomaly Detection, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging Face, PyTorchAI Models
ChatGPT, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$750
|
Standard
$1,250
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 14 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 | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$150
30-min follow-up call 2 weeks after delivery
+$99Frequently 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
AWS Certified Solutions Architect | RAG, AI Agents, Serverless, DevOps
100%
Job Success
Surat, India - 3:20 pm local time
I design and build production systems across backend engineering, serverless architecture, DevOps, cloud infrastructure, MLOps, RAG, and AI agents. Reliability, security, observability, and cost control are built in from the beginning.
Expert-Vetted, Top 1% on Upwork
100% Job Success | $100K+ earned | 2,700+ hours | 41 jobs
9+ years of production engineering
3x AWS Professional Certified | 5x AWS Community Builder
Clients hire me when they need a single senior engineer who can understand the entire system, make sound architectural decisions, and implement the solution.
CLIENTS BRING ME IN WHEN
• A SaaS, backend, or AI product needs production architecture and hands-on delivery
• An AWS platform has reliability, scaling, latency, security, or cost problems
• Serverless workflows fail under retries, concurrency, duplicate events, or partial failures
• Infrastructure and deployments are manual, inconsistent, or difficult to recover
• An AI agent works in a demo but fails with tools, permissions, state, retries, or live business data
• A RAG system retrieves weak context, produces poor citations, or lacks tenant-level access control
• Claude, OpenAI, or Amazon Bedrock must connect safely to real workflows
• AI or ML workloads need deployment automation, monitoring, rollback, retraining, or GPU optimisation
BACKEND AND SERVERLESS
• Python, FastAPI, Flask, Node.js, and TypeScript
• REST APIs, microservices, async workers, and multi-tenant SaaS backends
• Lambda, API Gateway, DynamoDB, SQS, EventBridge, and Step Functions
• PostgreSQL, Aurora, MySQL, Redis, ElastiCache, and OpenSearch
• Authentication, authorization, tenant isolation, API security, and rate limiting
• Idempotency, retries, ordering, concurrency control, dead-letter handling, and recovery
AWS PLATFORM AND DEVOPS
• Terraform, AWS CDK, CloudFormation, SAM, Helm, and Ansible
• Docker, Kubernetes, EKS, ECS, GitHub Actions, Jenkins, and CI/CD
• IAM least privilege, RBAC, VPC design, networking, secrets, and environment isolation
• CloudWatch, OpenTelemetry, Prometheus, Grafana, Datadog, and structured logging
• Blue-green and canary deployments, rollback controls, disaster recovery, and multi-region design
• Infrastructure governance, compliance automation, and AWS cost optimization
PRODUCTION AI, RAG, AND AGENTS
• LangGraph, LangChain, MCP, structured tool calling, and agent workflows
• Multi-agent orchestration, state, approvals, audit trails, and human review
• Enterprise RAG with hybrid retrieval, reranking, citations, and access control
• OpenSearch, Pinecone, Qdrant, pgvector, and FAISS
• Claude, OpenAI, Amazon Bedrock, and open-source models
• Evals, regression testing, tracing, guardrails, and deterministic fallbacks
• Real-time AI and voice systems using WebSockets, LiveKit, and OpenAI Realtime API
• Token, latency, infrastructure, and inference cost optimization
MLOPS
• SageMaker Pipelines, MLflow, DVC, model packaging, and versioning
• Automated training, validation, deployment, retraining, and rollback
• Production model serving, autoscaling, monitoring, and GPU optimization
SELECTED OUTCOMES
• Built event-driven services processing more than 5 million orders per month at sub-200ms latency
• Supported systems designed for 99.99% availability during peak traffic
• Reduced manual AWS environment setup effort by 70% using reusable infrastructure as code
• Reduced cloud operating costs by 20%
• Accelerated infrastructure deployments by 30% using reusable AWS CDK constructs
• Delivered more than 20 production releases per week with automated CI/CD and testing
• Migrated monolithic workloads to serverless APIs and containers, tripling throughput and reducing infrastructure costs by 30%
HOW I WORK
I do not begin by choosing a framework. I begin with your workflow, architecture, repository, data boundaries, permission model, failure modes, deployment process, traffic, latency, availability requirements, and operating cost.
Then I identify the highest-risk path, design the smallest safe solution, and implement it.
I will also tell you when not to build something. I have recommended deterministic workflows instead of agents, direct queries instead of RAG, and simpler AWS services instead of platforms that add unnecessary cost or operational complexity.
I support architecture reviews, new platform implementation, production rescue, serverless backend development, RAG and AI agent productionization, MLOps platforms, DevOps modernisation, fractional principal engineering, and long-term contracts.
I work best with funded startups, SaaS companies, AI product teams, and enterprise engineering organisations that have a real product, workflow, codebase, or production problem.
Send me your architecture, repository context, infrastructure challenge, or production issue. I will identify the highest-risk constraint and the smallest practical step forward.
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.
Step 1 — Kickoff and workflow deep-dive
60-minute call. We map the business workflow, the systems the agent touches, data and permission boundaries, and what success looks like in 30 days.
Step 2 — Agent architecture draft
I design the core: what the agent decides vs what stays deterministic, tool boundaries, state management, human approval gates, guardrails, and failure and fallback paths.