DevOps Engineering Training in Pune with Placement

Every tool in this course runs on Linux, and the engineers who struggle later are always the ones who skipped this week. The file system hierarchy and what actually lives where, users, groups and permission bits including setuid and sticky, processes and signals, and how systemd starts and supervises everything on a modern distribution.

The lab work is deliberately diagnostic: you are given a broken service and asked to find it with `journalctl`, `systemctl status` and `ps`, rather than being shown the answer first.

The second half of the foundation. TCP/IP addressing and subnetting, DNS resolution end to end, HTTP and HTTPS including what a TLS handshake actually exchanges, and the diagnostic toolkit — `curl`, `dig`, `ss`, `netcat`, `tcpdump` — used against real failures rather than in isolation.

Then Bash as an engineering language, not a scratchpad: `set -euo pipefail`, quoting rules, exit codes and traps, argument parsing, and `shellcheck` in CI. Scripts you write this week are the ones you will reuse for the rest of the course.

Git taught as the working subset a DevOps engineer needs under pressure — commit, branch, merge, rebase, cherry-pick, reflog — plus conflict resolution done without losing work, which is the skill that separates confidence from fear.

Then the team layer: trunk-based development versus Git Flow and why most teams moved, branch protection rules, CODEOWNERS, conventional commits, semantic versioning, and pre-commit hooks that catch problems before CI has to.

Containers from the kernel features up — namespaces and cgroups — so that image layers, the build cache and copy-on-write filesystems are mechanisms you understand rather than behaviour you memorise. Dockerfile authoring with layer ordering that actually caches, multi-stage builds, distroless and Alpine base images, and BuildKit with Buildx for multi-platform output.

Security is taught inline, not bolted on: non-root users, read-only root filesystems, dropped capabilities, healthchecks, and Trivy scanning wired into the build.

The control plane explained as a set of cooperating loops — API server, etcd, scheduler, controller manager, kubelet, kube-proxy — because almost every Kubernetes debugging question resolves to knowing which component owns the behaviour.

Then the workload objects: Pods and why you rarely create them directly, Deployments and ReplicaSets, StatefulSets and their ordering guarantees, DaemonSets, Jobs and CronJobs. Rollouts, rollbacks and deployment strategies close the week.

The three areas that generate the most production incidents. Services and the four types, DNS inside the cluster, Ingress with the NGINX controller and TLS termination, and Network Policies with Calico or Cilium for east-west control.

Configuration follows — ConfigMaps, Secrets and why Secrets are only base64 until you add encryption at rest — then storage: persistent volumes, claims, storage classes and dynamic provisioning. Namespaces, RBAC, resource quotas and the Horizontal Pod Autoscaler complete the picture.

Packaging and running clusters rather than single workloads. Helm 3 charts, values files, templating, hooks and release lifecycle — then Kustomize, and an honest comparison of when each is the better answer instead of a preference stated as fact.

Operators and Custom Resource Definitions explain how the ecosystem extends Kubernetes itself. Cluster operations closes the week: taints and tolerations, affinity rules, pod priority and preemption, cluster autoscaler and Karpenter, and node upgrades performed without dropping traffic.

Pipelines as the product a DevOps engineer is actually judged on. Workflow and job structure, matrix builds, reusable and composite workflows, self-hosted runners, and caching strategies that take a slow pipeline from fifteen minutes to three.

OIDC federation to AWS and Azure is taught as the default rather than an advanced topic — long-lived cloud keys in CI are now a finding in any security review. The week ends with a complete build → test → scan → deploy pipeline for the container from week 4.

Jenkins remains the incumbent across a large share of Pune enterprise and services accounts, so this course teaches it properly rather than treating it as legacy. Declarative pipeline syntax, stages and parallelism, credentials binding, and the multibranch pipeline model.

Shared libraries are the module's centre — how large organisations remove duplication across hundreds of pipelines — followed by running Jenkins agents on Kubernetes, and artefact management with Nexus, Artifactory or GHCR.

Infrastructure defined, reviewed and versioned like application code. Terraform 1.7+ providers, resources, variables and outputs; remote state with locking and why state is the thing you protect above all else; modules for reuse; and workspaces for environment separation.

The operational half is what most courses omit — drift detection and remediation, importing resources someone created by hand, targeted applies during an incident, and `plan` output read as a code-review artefact. OpenTofu is covered as the licence-safe alternative.

The deployment model most Kubernetes-first teams have converged on: Git as the single source of truth, with an in-cluster agent reconciling reality toward it. Argo CD Application CRDs, sync policies, automated versus manual sync, self-healing and pruning, and how drift surfaces in the UI.

ApplicationSets handle multi-cluster and multi-environment generation, and promotion patterns — dev to staging to production — are built as a repository structure rather than a set of manual steps. Flux is covered comparatively.

Prometheus architecture, the pull model, exporters, scrape configuration and service discovery — then PromQL taught properly, because the difference between a useful alert and a noisy one is almost always the query. Operators, functions, rate versus increase, recording rules and their cost.

Alertmanager covers routing, grouping, inhibition and silences. Grafana dashboards are built against the USE method and the Four Golden Signals rather than assembled from whatever panels look impressive.

The other two pillars. Loki for logs with LogQL, label cardinality as the thing that decides whether a logging stack stays affordable, and structured logging conventions applied at the application side. Tempo for distributed traces, span context propagation, and reading a trace to find the slow hop.

OpenTelemetry ties it together as the vendor-neutral instrumentation standard — SDKs, the collector, and correlating a metric spike to the logs and traces behind it. The week ends with an on-call simulation against a deliberately broken cluster.

Security applied to everything already built. Pod Security Standards and Pod Security Admission, policy enforcement with OPA Gatekeeper or Kyverno, and runtime detection with Falco. RBAC revisited adversarially — what a compromised service account can actually reach.

Secrets management moves beyond Kubernetes Secrets to Sealed Secrets and the External Secrets Operator. The supply-chain half covers image signing with Cosign, SBOM generation, dependency and image scanning in CI, and the admission controls that stop an unsigned image from ever running.

The practices that turn a DevOps engineer into a platform or SRE hire. Cost visibility with tagging, cost allocation and Kubecost; right-sizing requests and limits from real utilisation data; spot and preemptible capacity for interruptible workloads; and the standard Kubernetes waste list.

Reliability follows: SLIs, SLOs and error budgets as a decision-making tool rather than a dashboard, incident response and severity levels, blameless postmortems, and runbooks written to be usable by someone who did not build the system.

Two weeks of full-time capstone work followed by structured interview preparation. You build and document a complete delivery platform — infrastructure in Terraform, workloads on Kubernetes, GitOps deployment through Argo CD, a full observability stack, and security policy enforced in CI.

Interview preparation is run as three separate rounds because that is how Pune DevOps hiring actually works: a Linux and scripting round, a Kubernetes troubleshooting round against a broken cluster, and a system-design round. Resume, LinkedIn and GitHub polish included.