SB
Spandana Bala
DevOps Engineer
Hyderabad · India
Now at · Microsoft
Multi Cloud DevOps · AWS · Azure · Kubernetes · Azure DevOps · AIOps · Enrolling now
Multi Cloud DevOps & AI
A career-focused, hands-on program: build strong Linux, Git, Docker, Kubernetes and CI/CD foundations, then go deep on both production clouds — AWS and Azure — with Azure DevOps, Terraform and observability, and run them with AI-assisted operations, agents and MCP.
100K+
alumni community
1,000+
hiring partners
4.8/5
avg class rating
12
partner centres
7
Edify Nuva centres
Where our DevOps alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer
What is the Multi Cloud DevOps & AI program?
A multi-cloud DevOps engineer builds and runs the delivery platform — pipelines, containers, Kubernetes, infrastructure as code and observability — across AWS and Azure, and now uses AI agents to operate it. Most DevOps courses stop at one cloud and one pipeline tool. This program ends only when you have provisioned EKS and AKS from Terraform, delivered the same application through Jenkins, GitHub Actions and Azure Pipelines, wired GitOps, monitoring and security scanning, and put an operations agent with MCP access under guardrails.
The complete delivery chain
Twelve links, one owner — end to end.
01FOUNDATIONS
- •Linux, Bash & Python for DevOps
- •Git, GitHub & branching strategies
- •Docker, registries & image security
- •Cloud & networking fundamentals — AWS & Azure
02BUILD & DELIVER
- •Kubernetes on EKS & AKS with Helm & ArgoCD
- •Jenkins, GitHub Actions & Azure Pipelines
- •Azure DevOps — Boards, Repos, Artifacts, Test Plans
- •Terraform, Bicep & Ansible across both clouds
03RUN & GOVERN
- •Prometheus, Grafana, logging & tracing
- •DevSecOps — scanning, secrets & compliance
- •AIOps, DevOps agents & MCP for infrastructure
- •FinOps, SRE practices & certifications
DevOps in 2026
Two clouds, one platform. And agents now help run it.
Multi-cloud is the enterprise defaultCloud layer
Most large organisations run AWS and Azure side by side — and Indian GCCs in banking and insurance lean Azure. Engineers who can provision, secure and operate both are the ones who get hired.
Azure DevOps & GitHub togetherDelivery layer
Azure Boards, Repos, Pipelines and Artifacts remain the ALM standard in Microsoft-centric enterprises, while GitHub Actions and Copilot lead new projects. Fluency in both — plus Jenkins for the installed base — is expected.
Kubernetes, GitOps & platform engineeringPlatform layer
EKS and AKS with Helm, ArgoCD and internal developer platforms are how teams ship. Platform engineering turned DevOps into a product for developers.
Infrastructure as code everywhereAutomation layer
Terraform across clouds, Bicep on Azure, policy as code with OPA and Checkov — reviewed, tested and increasingly generated by AI coding agents that engineers must verify.
AIOps & DevOps agentsNew '26
Amazon Q, Azure Copilot, Datadog and PagerDuty AI triage alerts and draft remediations; agents with MCP access to AWS, Azure and Kubernetes execute runbooks under approval. Operating them safely is a new DevOps skill.
DevSecOps, FinOps & complianceGovernance layer
Supply-chain security, secrets management, CIS benchmarks and cost governance are audit requirements — and now extend to what AI agents are allowed to change.
What this means for your career: DevOps roles now ask for two clouds, Kubernetes, IaC and AI-assisted operations together — the differentiator is a multi-cloud platform you built end to end with pipelines, observability and a governed operations agent, not a single-tool certificate.
Who should join
Built for people moving into cloud and DevOps engineering.
Graduates & career switchers
System administrators & support engineers
Developers moving to DevOps or platform roles
Testers & release engineers
Single-cloud engineers adding AWS or Azure
Engineers targeting GCC & enterprise roles
Prior experience: none required — Linux, scripting and cloud are taught from scratch. The program builds foundations before Kubernetes, both clouds, Azure DevOps, IaC and AI operations.
What you will be able to do
Build the platform on both clouds — and run it with AI.
Automate on LinuxLinux administration, Bash and Python for DevOps, Git and GitHub workflows.
Run containers at scaleDocker, Kubernetes on EKS and AKS, Helm, ArgoCD and GitOps.
Deliver with three pipeline platformsJenkins, GitHub Actions and Azure Pipelines with quality gates, plus the full Azure DevOps ALM stack.
Architect on AWS and AzureIAM and Entra ID, compute, networking, storage, monitoring and cost on both clouds.
Provision and secure with codeTerraform, Bicep and Ansible; Prometheus, Grafana, tracing, DevSecOps scanning and secrets.
Operate with AIAIOps, DevOps agents with MCP access to AWS, Azure and Kubernetes, and Claude Code for infrastructure.
Course curriculum
Twelve sections. 59 modules. Linux → Kubernetes → CI/CD → Azure DevOps → AWS → Azure → IaC → AIOps.
01
Fundamentals of IT, Cloud & AI
How software delivery, cloud and AI operations fit together — and where a DevOps engineer works in 2026.
Application lifecycle and Agile / Scrum
DevOps — the infinity loop and SDLC integration
Platform engineering and SRE roles
Career pathways and certifications — AWS, Azure, CKA
IaaS, PaaS and SaaS
AWS and Azure account structure, regions and pricing
Shared responsibility model
Choosing a cloud — and why multi-cloud
IP, DNS, ports and protocols
VPC and VNet, subnets, security groups and NSGs
Load balancers and gateways
Launching an EC2 instance and an Azure VM
LLMs, agents and MCP at a glance
AIOps and AI-assisted operations
Coding agents — Claude Code, Cursor, Copilot
Responsible AI and security basics
Terminal, Git and VS Code
AWS CLI and Azure CLI
Claude Code and Cursor for infrastructure code
Reviewing AI-generated configuration
02
Linux & Scripting
The operating system underneath everything — and the shell that automates it.
File system, permissions and users
Processes, services and systemd
Package management
Networking commands and troubleshooting
Variables, conditionals and loops
Functions and error handling
Text processing — grep, sed, awk
Automated deployment scripts
Python fundamentals for operations
boto3 and Azure SDK
Automation scripts and CLIs
APIs, JSON and YAML handling
SSH, keys and access control
Firewalls and logging
Cron and scheduled jobs
Security hardening basics
03
Git, GitHub & Collaboration
Source control and the workflows delivery teams run on.
Repositories, commits and branches
Merging, rebasing and conflicts
Tags and releases
Git internals at a glance
Forks, pull requests and code review
Branch protection and CODEOWNERS
Issues, projects and discussions
GitHub Copilot in review
GitFlow, trunk-based and GitHub Flow
Release and hotfix branches
Monorepos vs polyrepos
Choosing a strategy
04
Containers & Kubernetes
Package once, run anywhere — then orchestrate at scale.
Images, containers and registries
Dockerfiles and multi-stage builds
Volumes and networking
Docker Compose
ECR, ACR and Docker Hub
Image scanning with Trivy
Signing and SBOMs
Base image hygiene
Architecture — control plane and nodes
Pods, Deployments, Services and Ingress
ConfigMaps, Secrets and volumes
kubectl workflows
Namespaces, RBAC and network policies
Autoscaling — HPA, VPA, Karpenter
Resource limits and probes
Troubleshooting
Provisioning EKS and AKS
IAM / Entra ID integration
Cluster upgrades and node pools
Cost management
Helm charts and values
Kustomize overlays
ArgoCD and GitOps workflows
Progressive delivery with Argo Rollouts
05
CI/CD Pipelines
Three pipeline platforms — one mental model.
Continuous integration, delivery and deployment
Pipeline stages and artefacts
Deployment strategies — rolling, blue-green, canary
Quality gates
Installation and agents
Declarative pipelines and shared libraries
SonarQube and Nexus integration
Deploying to Kubernetes
Workflows, jobs and matrix builds
Reusable workflows and OIDC to cloud
Build, test, scan, push to ECR / ACR
Deploy to EKS / AKS with smoke tests
YAML pipelines, stages and templates
Variable groups and Key Vault
Environments, approvals and gates
Multi-cloud deployments from Azure Pipelines
Nexus and Azure Artifacts
Unit, integration and smoke tests in pipelines
SonarQube quality gates
Release management
06
Azure DevOps (ALM)
The complete Microsoft application lifecycle stack — a hard requirement for GCC and large-enterprise roles.
Work items, backlogs and sprints
Boards, queries and dashboards
GitHub integration
Process customisation
Git repositories and branch policies
Pull request workflows
Importing from GitHub
Security and permissions
Self-hosted agents and agent pools
Service connections to AWS and Azure
Templates and pipeline libraries
Deployment jobs and strategies
Package feeds — npm, Maven, NuGet, Python
Upstream sources
Test Plans and manual testing
Traceability from story to release
Organisations, projects and security
Auditing and compliance
GitHub Enterprise vs Azure DevOps
AZ-400 exam objectives
07
AWS Cloud
Solutions Architect depth on the largest cloud.
Users, roles and policies
Organizations and SCPs
Identity federation and SSO
Least privilege in practice
EC2 instance types and AMIs
Auto Scaling and load balancing
Lambda and API Gateway
ECS and Fargate
VPC design, subnets and routing
NAT, VPN and Transit Gateway
Route 53 and CloudFront
Security groups and NACLs
S3, EBS and EFS
RDS, Aurora and DynamoDB
Backups and lifecycle policies
Data protection
CloudWatch and CloudTrail
Cost Explorer and Trusted Advisor
Well-Architected Framework
AWS Solutions Architect Associate alignment
08
Azure Cloud
The enterprise cloud — from Entra ID to AKS, with AZ-104 and AZ-900 alignment.
Subscriptions, management groups and resource groups
Azure Portal, CLI and Cloud Shell
Azure Policy, tags and locks
Cost Management and Advisor
Entra ID users, groups and service principals
Managed identities
Role-based access control
Conditional access and PIM basics
Virtual machines and scale sets
App Service and deployment slots
Azure Functions
Container Apps and ACR
VNets, subnets, peering and NSGs
Load Balancer, Application Gateway and Front Door
VPN Gateway and ExpressRoute basics
Azure Firewall and Bastion
Storage accounts, Blob and Files
Azure SQL and Cosmos DB
Backup and Site Recovery
Data protection and encryption
Azure Monitor, Log Analytics and Application Insights
Microsoft Defender for Cloud
Key Vault
Landing zones and AZ-104 alignment
09
Infrastructure as Code & Configuration
Provision both clouds from code — repeatably and reviewably.
HCL, providers and resources
Variables, outputs and state
Plan, apply and destroy
Remote state with S3 and Azure Storage
AWS provider — VPC, EC2, EKS
Azure provider — VNet, VM, AKS
Modules and workspaces
Multi-cloud deployments
Bicep for Azure
ARM templates (overview)
CloudFormation for AWS
Choosing an IaC tool
Inventories, playbooks and roles
Idempotency
Ansible with cloud dynamic inventories
Configuration drift
Terraform validate, tflint and Checkov
Policy as code with OPA and Sentinel
Secrets management
AI-assisted IaC review
10
Observability, SRE & Security (DevSecOps)
Run systems people can trust — and prove it.
Metrics, exporters and PromQL
Grafana dashboards and alerts
Alertmanager
SLIs, SLOs and error budgets
ELK / OpenSearch and Loki
OpenTelemetry traces
CloudWatch and Azure Monitor correlation
Structured logging
Incident management and on-call
Post-mortems and blameless culture
Capacity planning
Chaos engineering basics
SAST, DAST and dependency scanning
Trivy, Snyk and SonarQube in pipelines
Secrets scanning and Vault
SBOMs and signing
AWS Security Hub and Microsoft Defender for Cloud
CIS benchmarks
Compliance evidence
Zero trust basics
11
AI for DevOps — AIOps, Agents & MCP
Use AI to run infrastructure — and govern it.
How LLMs work — tokens, context, cost
Structured outputs and tool calling
Hallucination and its operational consequences
Model selection
Claude Code and Cursor for Terraform, Kubernetes and pipelines
Spec-first infrastructure changes
Reviewing and testing generated IaC
Team conventions
Anomaly detection and alert correlation
LLM-assisted runbooks and root-cause analysis
Amazon Q, Azure Copilot and Datadog / PagerDuty AI
Human-in-the-loop remediation
Agent frameworks — Claude Agent SDK, LangGraph
Agents for triage, deployment and cost review
Guardrails, approvals and audit
Evaluating agent behaviour
Model Context Protocol basics
AWS, Azure, Kubernetes and GitHub MCP servers
Building custom MCP servers for internal tools
Security and permissions for agent access
Cost optimisation with AI
Policy and approvals for autonomous actions
Audit trails
EU AI Act and NIST AI RMF awareness
12
Capstone, Certifications & Career
A multi-cloud platform with pipelines, observability and an operations agent — verifiable by employers.
Terraform-provisioned EKS and AKS with networking and identity
Jenkins, GitHub Actions and Azure Pipelines delivering the same app
GitOps with ArgoCD, Prometheus / Grafana and security scanning
Public verification URL
Operations agent for triage and deployment checks
MCP servers for AWS, Azure and Kubernetes
Guardrails, approvals and audit
Demo and write-up
AWS Solutions Architect Associate
Microsoft AZ-900 and AZ-104
Microsoft AZ-400 DevOps Engineer Expert
CKA and Terraform Associate
GitHub portfolio with architecture diagrams
Resume rewrite around platforms shipped
DevOps interview practice — scenarios, troubleshooting, system design
Warm introductions to hiring partners
Tools you'll master
32+ DevOps & AI Ops tools, one production project.
Real-time projects
You don't watch videos. You ship software.
Three portfolio projects and a partner capstone, each threaded through the entire curriculum — Kubernetes, three pipeline platforms, both clouds, IaC and AI operations all land in real deliverables.
Hero project
Production multi-cloud platform with AI Ops
Build an end-to-end platform across AWS, Azure, and GCP — Terraform-baked infrastructure, ArgoCD-managed Kubernetes, Prometheus + Grafana + Loki + Tempo observability, and a LangGraph AI Ops agent that triages alerts and drafts post-mortems for the on-call SRE.
01Multi-cloud GitOps pipeline — Terraform + Packer baselines for AWS/Azure/GCP, ArgoCD-managed K8s manifests, branch-based environments, signed commits and policy-as-code.
02K8s platform with golden paths — Helm/Kustomize charts, secrets via Vault, service mesh, autoscaling, multi-region failover, blue/green + canary rollouts.
03Observability stack — Prometheus + Grafana + Loki + Tempo, SLOs and error budgets, on-call runbooks in Confluence/Notion, Datadog APM for app teams.
04AI Ops agent — a LangGraph agent that triages alerts, drafts post-mortems, suggests rollbacks, and exposes runbooks via an MCP server to incident responders.
Outcome: ~70% MTTR reduction
Deploy success: 99.95%
Reviewer: Senior SRE panel
TerraformK8sArgoCDPrometheusLangGraph
Enterprise
Cloud cost & FinOps agent
Wire AWS / Azure / GCP cost APIs into a daily reporting agent — anomaly detection on spend, recommendation engine for right-sizing, Slack alerts with AI-drafted business context.
FinOpsAWSAzureLangChain
Real-time
Self-healing K8s incident loop
Build a Prometheus → AlertManager → LangGraph triage agent → ArgoCD rollback loop with hand-off to human SREs only when the agent's confidence is below threshold.
K8sLangGraphArgoCDSelf-healing
Project
Your AI DevOps platform in a controlled project environment.
Pick a real partner platform team. Deploy a production multi-cloud GitOps + observability + AI Ops stack — Terraform infrastructure, ArgoCD pipelines, Prometheus stack, LangGraph triage agent — into a partner team that's running it for real users.
Download the real world project
Full scope, sample deployment contexts, project milestones, and grading rubric — PDF, 14 pages.
Production-style capstoneCareer support included
Your instructor
Taught by the engineer who ran your dream job's pipelines.
MK
Manikanta Kona
Founder, Edify Nuva · Principal DevOps Architect
AWS · Azure · GCP · Kubernetes · Terraform · ArgoCD · LangGraph · MCP
"A 2026 DevOps engineer doesn't just write Terraform and Helm charts. They run a
multi-cloud platform with GitOps rigor, ship a LangGraph AI Ops agent that triages alerts at 3am, and stake
an SLA on the whole stack — Prometheus SLOs, ArgoCD rollbacks, MCP tool policies and all. That's the bar I
teach to, every class."
15 yrs
PLATFORM ENG
2,400+
LEARNERS
4.8 /5
RATING
Manikanta is the founder of Edify Nuva and brings 15 years of multi-cloud platform engineering from AT&T, Salesforce, Cox Communications, and Broadcom — where he led GitOps adoption, Kubernetes platform builds, and cloud cost programmes for Fortune-500 banks, telcos, and insurers. Most recently he architected production AI Ops practices that pair Prometheus + Grafana observability with LangGraph triage agents and an MCP tool layer the on-call SRE team actually trusts in production.
His classes get you two things other programs don't give you: a founding architect who still ships production platforms, and a curriculum rewritten every quarter to match what hiring managers actually ask about — including multi-cloud GitOps, ArgoCD-driven rollouts, and AI Ops agents that operate alongside human SREs. M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, Edify Nuva · Platform Engineering & AI Ops Lead
K8s · Terraform · ArgoCD · Prometheus · LangGraph · MCP · Multi-cloud
"A multi-cloud production platform stops being a slide when you stake an SLA on it —
when GitOps pipelines, Prometheus SLOs, ArgoCD rollouts, and a LangGraph triage agent are the way the
on-call SRE actually works on a Tuesday at 3am. GitOps rigor and AI Ops adoption in incident response aren't
optional anymore. That's what I teach."
10 yrs
PLATFORM ENG
1,800+
LEARNERS
4.8 /5
RATING
Ravi is Chief Technologist at Edify Nuva, where he leads the platform engineering and AI Ops practice. After ~10 years building production multi-cloud Kubernetes platforms across enterprise teams, he stepped into the Chief Technologist seat to wire Terraform, ArgoCD, Prometheus, LangGraph, and MCP into the way SRE teams actually work — GitOps pipelines tuned to real incident patterns, MCP tool policies on-call engineers trust, observability with SLOs that matter, and AI Ops agents wired into incident response.
His AI Ops modules are built from real production post-mortems, not slide decks. Expect to leave with working Terraform multi-cloud baselines, ArgoCD GitOps pipelines, a Prometheus + Grafana + Loki + Tempo observability stack, an MCP server with auth + scope policy, and a LangGraph triage agent you can stake an SLA on. Hyderabad-based, hands-on, and known for the unglamorous parts of platform engineering that everyone else skips.
HIRING PARTNERS · INDUSTRY VOICES
What DevOps employers say about Edify Nuva grads.
Real feedback from platform and SRE leaders at AI-first companies and the firms hiring our Multi Cloud DevOps + AI Ops graduates.
Edify Nuva grads ramp 40% faster on platform rollouts than typical DevOps hires. Best Multi Cloud DevOps + AI Ops pipeline in India.
Aakash Mehta, SRE Director, Microsoft
We've onboarded 80+ Edify Nuva alumni in 18 months. Lowest ramp time we've seen for GitOps pipelines and AI Ops triage practices.
Anita Sharma, Senior Manager, Deloitte
The Multi Cloud DevOps programme is comprehensive — Terraform, K8s, ArgoCD, AI Ops. Grads come pre-trained for production multi-cloud platform engineering.
Rahul Bhatt, Solutions Lead, Mphasis
Their GitOps + AI Ops track produces PMs who ship production-grade pipelines on day one. Rare combination of SRE rigor and platform craft.
Deepak Pillai, Senior Architect, TCS
What sets Edify Nuva apart is the AI Ops layer baked into the DevOps track. Our enterprise clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their AWS Solutions Architect + CKA prep is rigorous, and the shipped project — GitOps pipeline, observability stack, AI Ops agent — is what closes interviews for us.
Vikram Iyer, Director, Infosys
Edify Nuva's DevOps engineers ship reliable platforms twice as fast in the first 90 days. Our internal platform metrics back this up clearly.
Lakshmi Nair, VP Engineering, Wipro
Best Multi Cloud DevOps + AI Ops pipeline we've sourced from in India. Their projects are real shipped pipelines, not slide demos.
Karthik Subramanian, Engineering Director, Cognizant
Strong multi-cloud and Kubernetes foundation. Their DevOps grads need almost zero ramp time on enterprise platform engagements with us.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ Edify Nuva alumni across our platform and watsonx engineering teams. Strong fundamentals, sharp on SLOs and on-call.
Sanjay Verma, Talent Director, IBM
GitOps + AI Ops triage is exactly the talent gap we've been struggling to close. Edify Nuva is filling it for us reliably.
Anjali Desai, Practice Head, LTIMindtree
Their DevOps track delivers engineers who navigate Terraform, K8s, and ArgoCD on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ Edify Nuva graduates for our platform engineering practice. Strong on Terraform, sharp on K8s, fluent in AI Ops.
Geetha Pillai, Talent Acquisition Lead, Cyient
Edify Nuva grads who blend platforms with Azure OpenAI Ops agents land production-ready on day one. Rare combination, well-trained.
Priya Reddy, Talent Lead, Microsoft
03Program certifications
An Agent‑Ready credential, not a participation trophy.
Edify Nuva · Institute Certificate
Agent‑Ready DevOps Engineer
Presented to
Spandana Bala
For the successful design, build, and production deployment of a multi-cloud platform —
GitOps pipeline, K8s platform, observability stack, and an AI Ops triage agent — evaluated against the AWS Solutions Architect, CKA (Certified Kubernetes Administrator), and Terraform Associate credential rubrics.
Manikanta Kona
CEO · Edify Nuva
AGENT
READY
2026
READY
2026
01
Industry‑recognized
Co‑branded with the platform engineering community and mapped to AWS Solutions Architect
and CKA (Certified Kubernetes Administrator) credentials — names that hiring managers already scan for on resumes.
02
Project artifact included
Every certificate carries your shipped project — GitOps pipeline, K8s platform, observability stack, AI Ops agent — with
a link to the live partner-org deployment. Proof, not a promise.
03
Enhanced skill validation
Graded against the 2026 Agent‑Ready rubric: GitOps pipelines, K8s platforms, observability stacks, AI Ops triage,
FinOps & SLOs. No pass/fail — a level 1‑5 band.
04
Verifiable on a public URL
Each credential has a public verification page recruiters can check in 10 seconds — no PDF
back‑and‑forth.
Job roles
Roles this program prepares you for.
DevOps Engineer
Pipelines, containers and infrastructure across AWS and Azure.
Cloud Engineer (AWS / Azure)
Provision, secure and operate cloud infrastructure.
Azure DevOps Engineer
Boards, Repos, Pipelines and Artifacts for enterprise delivery.
Site Reliability Engineer
Observability, SLOs, incident response and automation.
Platform Engineer
Kubernetes, GitOps and internal developer platforms.
Infrastructure as Code Engineer
Terraform, Bicep and Ansible at scale.
DevSecOps Engineer
Supply-chain security, scanning, secrets and compliance.
AIOps / Automation Engineer
AI-assisted operations, DevOps agents and MCP integrations.
Release / Build Engineer
Jenkins, GitHub Actions and Azure Pipelines release management.
Cloud / DevOps Architect (career path)
Grow toward designing multi-cloud platforms.
What employers should see in your portfolio: that you can build and run a platform on both clouds — Terraform-provisioned EKS and AKS, the same application delivered through Jenkins, GitHub Actions and Azure Pipelines, GitOps with ArgoCD, Prometheus and Grafana, security scanning, and an operations agent with MCP access under guardrails.
04Job placement support
Your first DevOps offer isn't a lottery ticket. It's a built process.
GitHub, LinkedIn, resume — and most importantly, warm intros into platform teams at AI-first companies. Our
placement team works your search like an account, not a helpdesk.
01 / GITHUB & PORTFOLIO
A portfolio, not a graveyard.
Guidance on building a portfolio that showcases your GitOps pipeline, K8s platform, observability dashboard, AI Ops agent, and a public verification URL — reviewed 1:1, not via template.
02 / RESUME PREP
Rewrite, don't proofread.
A one-page resume rebuilt around the platforms you shipped (GitOps pipelines, K8s clusters, AI Ops agents), the partner-org project, and the business outcome. Reviewed by platform leaders who've read 10,000+ resumes.
03 / LINKEDIN + INTROS
Where most opportunities actually live.
Profile tuning plus direct warm introductions into platform teams at AI-first companies — Microsoft, AWS, HashiCorp, Datadog, GitLab, Atlassian, Anthropic, Hugging Face, Databricks, Snowflake, Stripe, Razorpay, Freshworks, Zoho, plus services that staff platform teams (Deloitte, Accenture, Cognizant, TCS). You leave with recruiter contacts, not a generic "good luck."
DevOps alumni
Hundreds of DevOps careers launched — here are eight.
NV
Naveen Vedala
Senior SRE
Hyderabad · India
Now at · Atlassian
TA
Tejashwini Addla
Staff Platform Engineer
Hyderabad · India
Now at · Salesforce
TD
Tharunesh Dillikar
Principal DevOps Engineer
Seattle · United States
Now at · Datadog
MM
Mujahed Mohammed
Cloud Architect
Hyderabad · India
Now at · Databricks
BK
Bhargav Kumar Murala
Kubernetes Lead
Hyderabad · India
Now at · Adobe
SL
Sai Manasa Leburi
AI Ops Lead
New York · United States
Now at · Hugging Face
RD
Rahul Dhamma
Director of Platform Engineering
Hyderabad · India
Now at · HashiCorp
Our locations
Come chat with us — over coffee, or over Zoom.
One flagship campus in Hyderabad, plus online Principal DevOps Engineer classes running on Indian and US timezones.
Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave ·
Hyderabad, Telangana
Call
+91 8142998866
US desk
+1 256 388 7766
Hours
Mon–Sun · 7 AM–9 PM
Online class
Global
Weekend and evening DevOps classes running on IST and PST. Every online class ships
the same shipped project — GitOps pipeline, K8s platform, observability stack, AI Ops agent — as the on‑campus track.
Timezones
IST & PST
Format
Live + 1:1 mentorship
Admissions
ENROLLING NOW
FAQ
Questions we actually get — answered honestly.
Straight answers on prerequisites, the DevOps stack, certifications, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.
Do I need a CS background or prior cloud experience?
No on both counts. Roughly 40% of every class comes from non-CS streams — mechanical, electrical, BCom, BBA, sysadmins, and self-taught coders. The opening modules cover the Linux fundamentals, networking, and Git basics from scratch. What you do need is consistency and regular practice.
Will I actually run a production platform, or only do tutorials?
You actually run it. Every learner stands up a multi-cloud GitOps pipeline (Terraform on AWS/Azure/GCP), a Kubernetes platform with ArgoCD-managed deploys, a Prometheus/Grafana/Loki/Tempo observability stack, and a LangGraph AI Ops triage agent. The project runs on a partner platform — not a tutorial.
Which tools, clouds, and AI models will I use?
Clouds: AWS, Azure, GCP. IaC: Terraform, Packer, Ansible, Vault, Consul. K8s: Docker, Kubernetes, Helm, Kustomize, ArgoCD, Flux. CI/CD: Jenkins, GitHub Actions, GitLab CI, CircleCI. Observability: Prometheus, Grafana, ELK, Datadog, New Relic, Loki, Tempo, Jaeger. AI Ops: OpenAI, LangChain, LangGraph, MCP.
Will I prep for AIPMM DevOps Engineer and Pragmatic Principal DevOps Engineer certs?
Yes. The curriculum is mapped to the AIPMM DevOps Engineer track and the Pragmatic Principal DevOps Engineer credential. We run two full mock exams and reimburse the voucher fee on first-attempt pass.
How is the learning workload structured?
The program combines live mentor-led classes, guided labs, project work, and optional support sessions. An advisor can explain the current class format before enrolment.
Is placement support really 1:1, and which companies hire DevOps engineers?
Yes. Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. Edify Nuva does not guarantee an interview, offer, salary, employer, location, or timeline.
Online, weekend, or on-campus?
All three. On-campus at the Hyderabad flagship, live online (IST and PST classes), and a weekend track for working professionals. Every format ships the same shipped project — GitOps pipeline, K8s platform, observability stack, AI Ops agent — only the schedule changes.
What if I fall behind, or can't continue mid-class?
Freeze your seat for up to 90 days and rejoin the next class — no extra fee. TAs run catch-up sessions every Saturday for learners needing additional support, and recordings of every live session are available for the lifetime of your account.
Still have a question? Talk to an advisor — no slides, no pitch.
One million AI‑native professionals by 2027.
Let's put you in that number.
Book a 20‑minute advisor call. We'll map your current role to the right program, talk honestly about timelines, and walk you through a real class's project.
