About Anatoliy Kim

From Infrastructure to Platform Engineering: My DevOps Journey

Technology careers rarely follow a straight line.

Mine started with systems, networks, Linux servers, and infrastructure long before DevOps became a common job title. Over time, that foundation evolved into cloud infrastructure, Kubernetes, Infrastructure as Code, CI/CD, GitOps, observability, and production reliability.

Today, I work as a DevOps / Platform Engineer, focused on building and operating production infrastructure that development teams can rely on.

Where It Started: Infrastructure

My early career was built around traditional infrastructure.

I worked with enterprise networks, Linux and Windows servers, routing, VLANs, firewalls, DNS, VPNs, load balancing, and high-availability systems.

I designed infrastructure supporting more than 100 users across multiple locations and worked with infrastructure serving corporate telecom and ISP customers.

This period taught me something that remains important in my work today:

Infrastructure is not just about technology. It is about keeping systems available when people depend on them.

I learned to think about failure, redundancy, networking, security, monitoring, and recovery long before I started working with Kubernetes.

My earlier infrastructure experience included maintaining mission-critical connectivity with 99.5% uptime, designing redundant server clusters, implementing load balancing and failover, and automating monitoring and maintenance processes.

Returning to Engineering Through DevOps

After spending years in operations and leadership roles across different industries and countries, I returned my focus to hands-on infrastructure engineering.

The industry had changed significantly.

Infrastructure was increasingly becoming code. Applications were becoming containerized. Kubernetes was becoming a standard platform for running distributed workloads. CI/CD and GitOps were changing how teams delivered software.

I decided not simply to learn individual tools, but to understand how these technologies fit together into a modern engineering platform.

That meant going deeper into:

Linux → Networking → Docker → Kubernetes → Terraform → CI/CD → GitOps → Observability → Security

I continue to develop this foundation through structured training, including EPAM UpSkill, School 21, and KodeKloud, while applying these technologies directly to production infrastructure.

Kubernetes Changed the Scale of the Work

One of the most important stages of my DevOps career was working with production Kubernetes infrastructure in a regulated FinTech environment.

I operated a 145-node Kubernetes production cluster supporting more than 2.5 million active users.

The environment included more than 50 microservices across 30+ namespaces, with development, staging, and production workloads requiring isolation, controlled access, monitoring, and reliable delivery.

At that scale, Kubernetes is no longer simply a technology to learn.

It becomes a platform that engineers depend on every day.

My responsibilities included cluster operations, workload management, RBAC, CI/CD, AWS infrastructure, observability, incident response, and troubleshooting across Kubernetes, networking, and databases.

This experience changed the way I think about DevOps.

The question is no longer:

"How do I deploy this application?"

It becomes:

"How do I build an environment where dozens of services and engineering teams can deploy safely and repeatedly?"

Automation Is About Removing Unnecessary Complexity

One of my main areas of focus became automation.

I worked with Terraform, AWS, GitHub Actions, GitLab CI, Docker, and Argo CD to replace manual processes with repeatable workflows.

For CI/CD, I created reusable workflows and integrated automated unit, integration, and end-to-end testing.

The result was a 70% reduction in production deployment errors and a 35% reduction in post-deployment rollbacks.

For me, this is what good automation means:

The goal is not to automate everything. The goal is to make the correct path the easiest path.

From VMs to Kubernetes

At UZINFOCOM, I worked on modernizing legacy workloads and moving applications from bare-metal and VM infrastructure into Kubernetes.

I led the migration of 30+ applications, using containerization and blue-green deployment strategies.

The migration reduced application downtime by 80% while helping establish more standardized deployment practices across engineering teams.

Migration projects are particularly interesting to me because they require more than Kubernetes knowledge.

You have to understand:

the existing infrastructure
networking
storage
application dependencies
deployment processes
failure modes
security
operational requirements

Then you have to introduce the new platform without breaking the existing system.

That is where my earlier infrastructure background becomes particularly valuable.

GitOps: Infrastructure and Applications Become Repeatable

Another important step in my development was adopting GitOps as a standard way of managing application delivery.

I implemented Argo CD and GitLab CI to automate application promotion between environments and standardize deployment workflows.

GitOps changed the relationship between infrastructure and application delivery for me.

Instead of asking:

"What did someone change in the cluster?"

you can ask:

"What changed in Git?"

That creates a much stronger operational model around:

Version Control → Review → Automation → Deployment → Observability → Rollback

It also makes infrastructure and application operations easier to reason about as systems become more complex.

Production Reliability Is More Than Monitoring

Operating production systems also taught me that monitoring alone is not observability.

I have worked with Prometheus, Grafana, VictoriaMetrics, Loki, and OpenTelemetry, building metrics and logging infrastructure across more than 50 microservices.

But the important part isn't the toolset.

The important questions are:

Can we detect a problem?
Can we understand its impact?
Can we identify the root cause?
Can we recover quickly?
Can we prevent the same problem from happening again?

This led naturally into incident response, root-cause analysis, runbooks, and post-mortems.

I created troubleshooting documentation and decision trees that reduced DevOps escalations by 45%, allowing developers to resolve common infrastructure issues more independently.

Structured incident investigations and preventive changes also reduced recurrence across recurring issue classes by an average of 30%.

Security Became Part of Platform Engineering

Modern infrastructure cannot treat security as a separate activity.

My work has increasingly involved HashiCorp Vault, Kubernetes RBAC, AWS IAM, network policies, firewall controls, network segmentation, and identity-based access.

At UZINFOCOM, I integrated Vault with CI/CD pipelines for dynamic secrets and identity-based access, replacing Kubernetes secret deployment configurations.

This is another reason I see Platform Engineering as a natural evolution of DevOps.

A platform has to provide developers with more than a deployment mechanism.

It needs to provide:

Security + Automation + Reliability + Observability + Self-Service + Operational Consistency

Where I Am Now

Today, I describe myself as a DevOps / Platform Engineer rather than simply a DevOps Engineer.

My strongest areas are:

Kubernetes / AWS / EKS / Terraform / GitOps / Argo CDCI/CD / Observability / Linux & Networking / Security & Secrets Management

My production experience now spans environments from enterprise infrastructure and networking to large Kubernetes platforms supporting millions of users.

The common thread throughout my career has been infrastructure.

The tools changed.

The scale changed.

The architecture changed.

But the fundamental objective remained the same:

Build systems that are reliable, understandable, repeatable, and resilient when things go wrong.

What's Next

My next step is to continue moving deeper into Platform Engineering and SRE.

That means going beyond operating infrastructure and focusing more on the engineering of internal platforms: developer experience, automation, reliability, observability, security, and scalable infrastructure abstractions.

Longer term, I am interested in the intersection of infrastructure, automation, data, and AI infrastructure.

But I see that as an extension of the same engineering foundation rather than a completely different career.

I started with servers and networks.

Then came virtualization, containers, Kubernetes, cloud, Infrastructure as Code, CI/CD, GitOps, and observability.

The next stage is building platforms that make increasingly complex systems easier to operate.

That's the direction I'm continuing to build toward.

About Me