5.1 MTTR
−50%
LivePerson · AIOps pipeline
Source: INC-007I build and run AI agents, MCP servers and AIOps pipelines in production.
Role
Principal SRE
Cloud & AI Platform Architect
↑ Goes by Saad
Previously at
Day shift · nothing on fireNight shift · pager on, lights off
I make 3 a.m. pages rarer, shorter and a lot less dramatic.
I design systems that stay up, write the playbook for when they don't, and lately, teach AI agents to read that playbook first.
Same unit, other configurations: AI platform · Cloud architecture · Platform engineering Same history, different ordering.
Between deployments and available immediately. Open to Principal and Staff roles, and to fractional, consulting, teaching and co-founder work in SRE, cloud architecture, platform engineering and AI platforms. Hyderabad, remote-first. Open to relocation (US, Canada, UK, Singapore, Gulf) with visa sponsorship.
11+ years running production platforms on GCP, AWS and Azure at LivePerson, SADA, EPAM, SAP Labs, Deloitte and Infosys. These days I build AI agents that triage incidents, cloud foundations that survive bad days, and platforms engineers actually like using.
Measured monthly. Every month operational; the orange ticks are standby.
Agents that triage alerts, investigate incidents and run runbooks. MCP servers give them tool access, a human approves every action, and evals check their output before release.
Stack
Ran at LivePerson
LLMs wired into internal systems with tool use, structured outputs and routing between providers. RAG pipelines and multi-agent workflows are tested against golden datasets before they ship.
Stack
Ran at LivePerson · SADA
Landing zones and foundations built from scratch, migrations planned end to end, and multi-region, multi-cloud designs on GCP and AWS that hold up on bad days.
Stack
Ran at SADA · SAP Labs · Infosys
Recovery designed and rehearsed before it is needed. For a critical banking system that meant cutting recovery from hours to minutes and losing almost no data.
Stack
Ran at SADA
SLOs and error budgets agreed with the business, telemetry on OpenTelemetry, and ML anomaly detection that pages a person only when it matters.
Stack
Ran at LivePerson · SADA · SAP Labs
Clusters across GKE, EKS and AKS, and pipelines that make releases boring: GitOps, drift that reverts itself, and rollback without a war room.
Stack
Ran at SAP Labs · EPAM · Deloitte
Self-service infrastructure through templates and golden paths, so service teams ship without filing a ticket and waiting a week.
Stack
Ran at LivePerson · EPAM
Tagging policy, commitment planning and right-sizing, with AI-assisted recommendations, so the cloud bill stops being a surprise.
Stack
Ran at SADA · LivePerson
Landing zones that stay inside SOC 2 and HIPAA controls, a framework that took a healthcare agency to Authority to Operate, and guardrails for AI agents.
Stack
Ran at SADA · LivePerson
5.1 MTTR
−50%
LivePerson · AIOps pipeline
Source: INC-0075.2 On-call noise
−45%
LivePerson · 500+ alerts/day correlated
Source: INC-0075.3 Uptime
99.99%
Fortune 500 · multi-region, multi-cloud
Source: INC-0065.4 Recovery time
4h to <20m
Banking system · disaster recovery
Source: INC-0065.5 Apps migrated
300+
SADA · to GCP and AWS
Source: INC-0065.6 Cloud cost
up to −40%
SADA · FinOps for clients
Source: INC-0065.7 Deploy cycle
2 weeks to 4 hours
SAP Labs · CI/CD
Source: INC-0045.8 Years in production
11+
6 companies, zero regrets
Source: the ruler, section 03INC-008 Open
This page
Game day · chaos experiment
■ Blameless postmortem · written automatically
INC-007 Resolved
LivePerson
Principal Site Reliability Engineer
Jun 2025 – Nov 2026
500+ alerts a day. Most of them noise, all of them paging a human.
No correlation, no context. Engineers were pattern-matching dashboards at 3 a.m.
Built an AIOps pipeline on OpenTelemetry with ML anomaly detection, plus AI agents for triage, investigation and runbooks. Every agent action waits for a human to approve it.
Rolled out a Backstage + Crossplane developer platform for 50+ engineers and LLM-assisted Terraform review, so infrastructure changes shipped noticeably faster.
On-call noise down 45%. MTTR down 50%. Mentored a team of 8 along the way.
INC-006 Resolved
SADA (An Insight Company)
Cloud Architect
May 2022 – May 2025
300+ applications to move to GCP and AWS, and a critical banking system that needed 4 hours to recover.
Built GCP foundations from scratch, planned the migrations, designed multi-region architecture and disaster recovery. Brought LLM-assisted Terraform and PR review to client work from 2023.
Banking RTO from 4h to under 20m, RPO from 1h to under 5m. 99.99% uptime, multi-region and multi-cloud, for a Fortune 500 client. Infrastructure costs down by up to 40% across 15+ engagements.
INC-005 Resolved
EPAM Systems
Lead DevOps Engineer
Nov 2021 – May 2022
GitOps with GitHub Actions and ArgoCD, so drift gets detected and reverted on its own. A reusable Terraform module library.
Environment provisioning from days to under 30 minutes. Led a team of 6 through 4 engagements with zero critical incidents.
INC-004 Resolved
SAP Labs
Sr DevOps Engineer
Jan 2020 – Nov 2021
Owned the GKE clusters as Product Owner across 100+ environments. CI/CD with Jenkins, Spinnaker and Ansible. Standard Prometheus and Grafana monitoring.
Deployment cycle from 2 weeks to under 4 hours, zero downtime. MTTR from 4 hours to 30 minutes.
INC-003 Resolved
Deloitte USI
DevOps Engineer
Oct 2017 – Jan 2020
Terraform and Ansible for 10+ enterprise clients: 2 days → 30 minutes. Jenkins pipelines running 50+ deploys a day with automatic rollback.
INC-002 Deployed
Infosys
DevOps Engineer
Jun 2015 – Oct 2017
Helped move 20+ client applications from data centres to AWS. Automated away 30+ hours of manual work a week.
INC-001 Deployed
MJCET, Osmania University
B.Tech, Computer Science and Engineering
2011 – 2015
Computer science degree, and head of Microsoft's student ambassador programme on campus. Builds have been passing since.
| Symptom | Probable cause | Remedy | See |
|---|---|---|---|
| The pager fires 500+ times a day | No correlation, no context: every alert pages a human | An AIOps pipeline on OpenTelemetry with ML anomaly detection, and AI agents that triage first | 4.14.5INC-007 |
| Recovery takes four hours | Disaster recovery was never designed or rehearsed | Multi-region architecture, RTO and RPO targets, and runbooks rehearsed before they are needed | 4.4INC-006 |
| A release takes two weeks | Releases are an event, with a war room for every rollback | CI/CD and GitOps that make releases boring, with zero downtime and automatic rollback | 4.6INC-004INC-003 |
| Environments drift apart | Changes are made outside Git and nobody reverts them | GitOps with GitHub Actions and Argo CD, so drift is detected and reverted on its own | 4.6INC-005 |
| A new environment takes days | Provisioning means filing a ticket and waiting | Self-service templates, golden paths and a reusable Terraform module library | 4.7INC-005INC-003 |
| The cloud bill is a surprise | No tagging policy, no commitment plan, nothing right-sized | Tagging policy, commitment planning and right-sizing, with AI-assisted recommendations | 4.8INC-006 |
| An AI agent wants production access | No guardrails between the model and the tools | MCP servers for tool access, a human approving every action, and evals before release | 4.14.9INC-007 |
Also available as
Not sure which fits? Describe the problem and I will suggest one.
Page SaadSame unit, other configurations: AI platform · Cloud architecture · Platform engineering Same history, different ordering.