80%
Less release time. More delivery momentum.
Strategized CI/CD processes and automated operational tasks with Jenkins and Python to compress the application release cycle by 80%—replacing release-window friction with repeatable flow.
Pune, India / Cloud + platform + AI engineering
I engineer the platform between code and customer.
Senior DevOps and cloud platform engineer combining 11+ years of systems experience with AWS, GCP, Kubernetes, Terraform, GitLab, and production-grade AI platform operations.
- 01 Automate
- 02 Architect
- 03 Observe
- 04 Augment
PROFILE / ACTIVE
● LIVE
AWS / GCP
GitLab
AI Engineering
Kubernetes
Linux
Terraform
raj@portfolio: ~/current-focus
⌘K
$ ./what-i-build --now
Reliable cloud platforms. Repeatable delivery. Intelligent operations.
$ stack --active
AWSGCPKubernetesTerraformGitLabAI PlatformsMLOps
01 / POSITION
I turn infrastructure from a bottleneck into a product - unifying DevOps, CloudOps, DevSecOps, SRE, systems engineering, MLOps, and platform thinking into reliable delivery.
02 / SELECTED IMPACT
Proof over promises.
I work where deployment pressure, production risk, and platform velocity collide—turning repeatable engineering into calmer operations and faster outcomes.
AI platforms built for the production pulse
Supporting an AI Marketplace and data-and-AI workloads on GCP with GKE, Terraform, GitLab delivery workflows, monitoring, incident response, and the operational discipline production AI demands.
Infrastructure change without the guesswork
Championed Terraform to replace manual provisioning with versioned, reviewable infrastructure—making cloud change faster to understand, safer to repeat, and easier to govern.
01
Won the AFour Hackathon
First place for problem solving with Python and OpenStack.
03 / SYSTEMS I SHAPE
From foundation to intelligence.
Cross-functional engineering depth for the moments when cloud, security, reliability, delivery, and AI workloads all become the same problem.
01
AWS & GCP cloud platforms
Engineering reproducible, secure cloud foundations across AWS and Google Cloud with infrastructure-as-code and an operations-first mindset.
- AWS
- GCP
- GKE
- Terraform
- IAM
02
Platform engineering & delivery
Building GitLab-driven paved roads that standardize how teams build, secure, release, and operate workloads.
- Kubernetes
- Docker
- GitLab
- CI/CD
- Jenkins
03
CloudOps, DevSecOps & SRE
Automating operations, shifting security into delivery workflows, and making production behavior visible, actionable, and recoverable.
- CloudOps
- DevSecOps
- Observability
- Automation
- SRE
04
AI platform engineering & MLOps
Supporting AI Marketplace, Spark, Ray, and data-platform workloads while exploring agents and tool calling for safer operational workflows.
- AI Marketplace
- MLOps
- Spark / Ray
- GKE
- GenAI
ROLE SIGNAL / CROSS-FUNCTIONAL
One engineering mindset. Six mission-critical roles.
Job titles change between organisations. The core mission does not: ship safely, operate reliably, secure the path, automate the toil, and give cloud and AI workloads a platform they can trust.
DevOps & Platform Engineer
GitLab and Jenkins pipelines, Kubernetes, Docker, Terraform, and paved-road automation that turns code into repeatable delivery.
DevSecOps Engineer
IAM, access and security coordination, reviewable change, delivery guardrails, and security treated as part of the engineering flow.
Cloud Engineer & SRE
AWS, GCP, GKE, observability, incident response, production continuity, and recovery-minded operations when the signal turns red.
Systems & CloudOps Engineer
Linux, Windows, networking, virtualization, automation, troubleshooting, and the deep infrastructure instincts modern platforms still depend on.
MLOps & AI Platform Engineer
AI Marketplace operations, Spark and Ray on GKE, data-platform collaboration, scalable compute, monitoring, and production readiness.
AI Engineer / AIOps Builder
Applied experiments with agents, RAG, tool calling, structured automation, and AI-assisted operational workflows—built with explicit guardrails.
04 / AI ENGINEERING LAB
Building the next layer.
A transparent look at the AI-enabled DevOps projects currently being explored and shaped.
LAB_01
AI Kubernetes Troubleshooter
Explain pod failures through cluster signals, events, logs, and resource state.
Kubernetes APIPythonTool calling
Blueprint
LAB_02
Job Application Copilot
Match roles, tailor truthful job-specific résumés, and organize a human-approved application flow.
LLMAutomationStructured data
Designing
LAB_03
AI Incident Commander
Correlate logs, metrics, and deployment context into a clear incident narrative.
ObservabilityAgentsFastAPI
Research
LAB_04
Terraform Intelligence
Generate and review secure cloud modules while keeping architecture decisions explicit.
TerraformGCPGuardrails
Next build
05 / JOURNEY
Built from the systems up.
My path has moved from core systems administration to delivery automation, platforms, cloud, and now applied AI.
2021 - PRESENT
T-Systems / Deutsche Telekom
Senior DevOps Consultant
AWS and GCP platforms, GKE, GitLab, Terraform, AI Marketplace operations, and data-and-AI workload reliability.
2018 - 2021
Mastercard Technology
Senior BizOps Engineer
CI/CD strategy, release automation, application modernization, Kubernetes deployment, and platform operations.
2015 - 2018
AFour Technologies
Senior DevOps Engineer
End-to-end delivery pipelines, containers, infrastructure as code, configuration management, and team enablement.
2014 - 2015
Rely Services
System Administrator
Linux and Windows systems, production servers, networks, troubleshooting, backups, access, and reliability foundations.
06 / AFK MODE
OFF KEYBOARD / SIGNAL RESTORED
Step away.
Reset the signal.
Return sharper.
Good engineering judgment is not built only inside terminals and incident channels. Off-keyboard time creates the distance to reset attention, notice patterns, and return ready for the next hard problem.
Calm inside the blast radius
When pressure spikes, reduce noise first: establish the signal, communicate clearly, and recover deliberately.
Perspective is operational leverage
Distance reveals what dashboards miss—simpler paths, hidden assumptions, and better questions for the team.
Sustainable intensity wins
High-impact engineering is a long game: protect focus, keep curiosity alive, and bring full energy when the moment matters.
07 / OPERATING PRINCIPLES
Simple systems.
Strong guardrails.
Clear signals.
- 01
Automate the repeatable
Keep human judgment for the work that actually needs it.
- 02
Design for recovery
Reliable systems assume that change and failure are normal.
- 03
Make decisions observable
A platform should explain what it is doing and why.
- 04
Use AI with guardrails
Augment engineers without inventing facts or hiding risk.
08 / CONNECT
Let’s build systems
that think ahead.
Building a DevOps, DevSecOps, cloud, platform, SRE, MLOps, or AI engineering team that needs production instincts and forward momentum? Let’s compare notes.