r/devopsGuru

How to start as a devops engineer? Also ano yung mga projects for beginners also as I am planning to upskill

Hi everyone,

We are currently using AWS SES for sending company emails. Previously, we were sending around 1 lakh emails per month, but recently our volume increased to around 10 lakh emails/month.

I started thinking about whether setting up our own mail server would be a better option, mainly to reduce costs. But I don't have much experience with running a mail server.

My main goal is cost saving. If maintaining our own server costs more (including infrastructure, maintenance, etc.), then I would prefer to continue with AWS SES.

I have a few questions:

• Is a self-hosted mail server actually cheaper at this scale (~10 lakh emails/month)?
• What kind of server/infrastructure would be required?
• How difficult is it to maintain good email deliverability compared to AWS SES?
• How do we manage IP reputation, warming, blacklists, bounce handling, spam complaints, etc.?
• Which tech stack/tools are commonly used for this? (Postfix, PowerMTA, Mailcow, etc.)
• Are there any legal/compliance requirements or documentation needed?
• What are the hidden costs and challenges people usually discover later?

Would love to hear from anyone who has moved from AWS SES/SendGrid/Mailgun to their own mail infrastructure, or anyone managing high-volume email servers.

Thanks in advance.

I’ve been interviewing for several Staff/Principal Platform Engineering and MLOps roles around Silicon Valley recently, and I’ve noticed an interesting pattern. Curious if others are seeing the same thing.

The job titles often sound like:

AI Platform Engineer
ML Platform Engineer
Platform Architect
Platform Engineering
Infrastructure Architect
MLOps Engineer

But once the technical interview starts, the discussion quickly narrows into Kubernetes operations.

Typical probing topics include:

Kubernetes scheduling internals
Pod lifecycle and failure scenarios
CNI/CSI details
ArgoCD deployment mechanics
Helm charts
Terraform modules and remote state
GitOps workflows
EKS/GKE operational issues
Container networking
Service mesh

Production debugging:
On-call incidents
Disk pressure / memory pressure
etcd behavior
Rolling upgrades

Very little time is spent discussing:
ML platform architecture
Feature stores
Model lifecycle
Training infrastructure
Batch vs streaming ML pipelines
Data contracts
AI infrastructure strategy
Platform architecture tradeoffs
Multi-team platform evolution

Instead, many interviews feel like they’re looking for someone who has spent years running production Kubernetes clusters and handling operational toil.
One hiring manager described the role as “Platform Engineering,” but nearly every technical question centered around daily Kubernetes operations, CI/CD mechanics, production troubleshooting, and infrastructure automation.

Compensation wasn’t low either. These were generally Staff-level roles in the Sunnyvale/Santa Clara area with base salaries around $220k–260k+, which surprised me because I expected more architectural discussion at that level. But Hiring manger puts a hard line of $200k max all inclusive. The JD compensation is fake to attract staff level candidates but pay was mid-level SRE $160-180k + bonus if any .

My impression is that many companies are using “Platform,” “AI Platform,” or “MLOps” as umbrella titles for what is fundamentally senior Kubernetes platform operations.

I’m not saying that’s wrong—someone has to build and operate reliable infrastructure—but the title and interview focus often don’t match.

Curious what others are seeing.

Questions for the community:
Are “Platform Engineering” and “MLOps” titles increasingly becoming Kubernetes operations roles?
How much architecture discussion do you typically see in Staff/Principal interviews?
Are companies intentionally broadening titles to attract candidates, or has the definition of platform engineering genuinely shifted toward infrastructure operations?
For those hiring Staff engineers, what percentage of the interview is architecture versus deep operational troubleshooting?
Interested to hear experiences from both hiring managers and candidates.

Disclosures: Used AI assistance to streamline my thoughts for better narrative and grammar.

I hope you don't mind me reaching out. I'm trying to better understand how platform and DevOps teams manage infrastructure requests, and I'd really appreciate your perspective.

If you have a minute, could you please share:

- Roughly how many infrastructure requests your team handles in a typical day or week?
- What types of requests you see most often?
- Which part of handling those requests tends to take the most time?

Even a brief reply would be incredibly helpful. Thanks so much!

I'm starting my DevOps journey and I'm looking for people who want to learn consistently together.

The goal is to keep each other accountable, discuss concepts, solve problems, and share progress while learning Linux, Git, Docker, AWS, Kubernetes, and other DevOps tools.

If you're interested, leave a comment below. We can connect here on Reddit first and decide the best way to organize once we have enough people.

We're building a multi-agent system for a large enterprise client. We have specialized agents that handle different tasks, one for data retrieval, one for analysis, one for reporting. They need to communicate with each other and with LLMs.

The communication patterns are getting complex. Some agents call other agents directly. Some call it LLMs. Some call tools. We need to trace requests across all these interactions for debugging and compliance. We also need to enforce access control, which agents can talk to which other agents.
We've looked at A2A (Agent-to-Agent protocol) and MCP for tool calling. But we're not sure how to operationalize this at scale. Do we need an Agent Gateway that sits between all these interactions? What does that even look like?

Has anyone deployed an Agent Gateway in production? What worked and what broke? Are there open source options or is this still a bleeding edge?

Guys can we all come together and provide all the devops or sre interview questions which you were asked in recent interviews, kindly comment the questions in comments section so we can all prepare

I wrote a post on how to generate RED metrics from your traces at the Collector before they hit your backend and why you'd want to do that instead of letting your backend handle it.

I also added some tips on how not to blow up your metric cardinality in the process.

Salutare.

Ieri am lansat versiunea beta a ultimului meu proiect personal: https://devopsacademy.space

✅E o platforma de IT Knowledge & DevOps unde inveti ca intr un joc RPG. Iti alegi o clasa de erou si intri intr o lume medievala, unde Linux e un regat, Git e o cronica regala, Docker e o fortareata. 🛡️🐳🐧⚔️

✅Se adreseaza celor care sunt interesati sa faca switch in IT, studenti, copii, oricine porneste de la 0, dar si developerilor care fac switch catre devops din cauza AI.

✅E un MVP, ceea ce inseamna ca ii testez potentialul prin feedbackul vostru.

Daca aveti timp…Multumesc! :)

1. Proficiency in Azure cloud services, including virtual machines, containers, networking, and databases.
2. Experience in designing, implementing, and managing Continuous Integration/Continuous Deployment (CI/CD) pipelines using Azure DevOps, Jenkins, or similar tools.
3. Knowledge of Infrastructure as Code tools like Terraform, ARM templates, or Azure Bicep for automating infrastructure deployment.
4. Expertise in version control systems, particularly Git, for managing and tracking code changes.
5. Strong PowerShell, Bash, or Python scripting skills for automating tasks and processes.
6. Experience with monitoring and logging tools like Azure Monitor, Log Analytics, and Application Insights for performance and reliability management.
7. Understanding security best practices, including role-based access control (RBAC), Azure Policy, and managing secrets with tools like Azure Key Vault.
8. Ability to collaborate effectively with development, operations, and security teams, with strong communication skills to drive DevOps culture.
9. Knowledge of containerization technologies like Docker and orchestration platforms like Kubernetes on Azure Kubernetes Service (AKS).
10. Strong problem-solving abilities to troubleshoot and resolve complex technical issues related to DevOps processes.

What other skills would you add to this list?

Hi everyone,

AI has become incredibly useful for software development, coding assistants, code reviews, and debugging. However, I don't see nearly as much discussion about applying AI to infrastructure and platform engineering.

I work mainly with Linux, Kubernetes (AKS, EKS, and GKE), observability tools like Grafana and SigNoz, and cloud infrastructure in general. I'm interested in finding practical ways to use AI beyond simply generating scripts or YAML files.

I'm looking for real-world use cases such as:

• Incident detection and root cause analysis
• Kubernetes troubleshooting
• Performance optimization and capacity planning
• Log and metrics analysis
• Infrastructure automation
• Cost optimization
• Security and compliance
• Anything else you've successfully implemented

If you're already using AI in your infrastructure or SRE/Platform engineering workflows, I'd love to hear what you're doing. What tools have actually improved your day-to-day work, and what hasn't lived up to the hype?

Thanks!

Hello everyone, 🤝 I am learning DevOps. If anyone is interested in joining me to practice together, please send me a direct message. 📩

Hello everyone, 🤝 I am learning DevOps. If anyone is interested in joining me to practice together, please send me a direct message. 📩

So I’ve been in IT for 26 years and grown with things in a very broad sense. Have been IT Director and Systems Admin…

There is a very specific role for a person who knows Azure Devops Labs and will work with a team working on API’s. How long does it take to get a grasp on this?

Is there a good learning resource or class someone here has taken? Anyone here want to help me I would pay… sometimes the guy on the ground is better than the one by the chalkboard…

Thanks a lot!
T.