I help AWS-based SaaS and enterprise teams productionize AI systems and rescue unreliable cloud platforms.
For CTOs, VPs of Engineering, Heads of AI and Platform, and technical founders facing reliability, permissions, observability, cost, deployment, or rollback risk in a production system or mature proof of concept.
Prefer to write first? Email Rahul
$ tail -f prod/incidents.log
03:47:12PagerDuty ALARM token-spend-anomaly sev1
03:52:40root cause: unbounded retry loop, no cost ceiling
04:15:03mitigated. runaway spend stopped: ~$40K
04:15:09action: cost ceilings now default on every LLM call
Call me when
Most teams don't need more cloud jargon. They need to know what's expensive, what's fragile, what slows users down, and what can be safely changed this week.
Finance sees the number. Engineering sees hundreds of services. Nobody has a safe first move.
I start with cost allocation, idle inventory, traffic shape, and commitment risk before touching architecture.
The feature looks impressive in a meeting and then becomes expensive, slow, or untrusted with real users.
I inspect retrieval, context design, evals, fallbacks, and cost per request before blaming the model.
Every release needs extra human attention. That slows the team and creates fear.
First I check blast radius, deployment path, stateful dependencies, and rollback ownership.
Dashboards are present, but incidents still become log archaeology and Slack guessing.
I connect symptoms to traces, metrics, logs, ownership, and alarms that point to action.
Services and AI tools can reach more than they should, while auditability and human approval remain unclear.
I define account boundaries, least-privilege tool access, approval controls, audit evidence, and IaC ownership.
9+
years in production
AWS, platform, and GenAI delivery
5M+
monthly orders supported
High-scale commerce work
30-55%
cloud cost reduction
Up to 55% on selected sanitized work
99.99%
platform uptime
Selected high-scale commerce work
₹50L+/year in AWS waste removed for clients (~$60K+/year), from cost and reliability audits. Selected metrics are from projects where public or sanitized detail can be shared. Quotes reflect real engagements. Named references available under NDA for qualified inbound.
Start with a bounded production audit. Then use your own team, bring me in for a focused productionization or rescue phase, or retain principal-level architecture ownership.
From $2,000 · one bounded system · report in 10 business days
AWS-based SaaS and enterprise teams approaching launch or carrying material production risk.
A paid, fixed-scope audit of one production system or mature AI workflow: reliability, IAM, data boundaries, observability, cost, deployment, and rollback.
What's included
Includes a focused AWS Cost & Reliability Audit when the bill is the symptom.
After evidence
Paid engagement · fixed scope
Move a mature AI prototype into controlled production with narrower tool permissions, stronger retrieval evidence, evals, fallbacks, human approval, and workflow-level cost controls.
Discuss productionizationAfter evidence
Paid engagement · scoped phase
Stabilize an expensive, fragile, undocumented, or partially abandoned AWS platform without beginning with a risky rebuild.
Discuss a platform rescueAfter evidence
Monthly retainer · scoped hours
Principal-level AWS and GenAI ownership without waiting for a full-time hire or bringing in a large consultancy.
Discuss fractional ownershipNot thumbnail galleries. The useful parts are what failed, what changed, what improved, and what trade-off was accepted.
Enterprise Knowledge Base
30+ min document hunts → sub-10s answers; hybrid RAG + semantic cache cut LLM spend ~60%.
KFC Thailand · Imaginato
Peak-hour monolith failures + inflated EC2 → 5M+ orders/mo, P99 <200ms, ~30% infra reduction.
ASTM International
Manual VPC sprawl + quarterly audit pain → standardized IaC, 70% faster setup, findings down 47 → 3.
Rahul migrated our entire ordering pipeline to serverless and cut our AWS bill by 30% while handling 5M+ orders per month. He doesn't just architect: he ships production infrastructure.
Engineering Lead
Imaginato (KFC Thailand)
We needed someone who could design a multi-tenant SaaS architecture and actually build it. Rahul delivered 99.9% uptime with a 40% cost reduction. His Terraform modules are still the foundation of our infra.
Founder & CEO
ProdigyBuild
Rahul brought order to our chaos. He standardized our cloud architecture across teams with IaC templates that reduced manual setup by 70%. SOC2 compliance went from months to weeks.
Director of Engineering
ASTM International
Quotes reflect real engagements. Named references available under NDA for qualified inbound.
I specialize in systems that need to survive production: serverless migrations, AWS cost optimization audits (typically 30-40%, up to 55% documented), event-driven architectures on ECS/EKS, GenAI/RAG infrastructure on AWS Bedrock, and multi-account cloud security. My value is highest when you need reliability, security, and cost control at scale.
Typical AWS + LLM engagements land around 30 to 40% when waste is structural: idle compute, wrong DB tier, unbounded model calls, missing lifecycle. On documented work, reductions reach up to 55%. I quote from utilization and billing data, not from a template. ₹50L+/year in AWS waste removed for clients (~$60K+/year), from cost and reliability audits.
I hold three AWS Professional certifications (Solutions Architect, DevOps Engineer, and Generative AI Developer), plus AWS Developer Associate and HashiCorp Terraform Associate. I'm also a 4x AWS Community Builder (Serverless).
Yes. I build production GenAI systems on AWS Bedrock including RAG pipelines, AI agents, and LLM applications. I handle the full stack: vector databases, prompt engineering, token cost monitoring, and audit logging. Currently studying Agentic AI at IIT Roorkee.
Start with one bounded system, the evidence you already have, and the production decision that cannot wait.
Prefer email? hello@rahulladumor.com
Request a paid reliability audit