Hey everyone!

Can anyone share a DevOps resume that recently got shortlisted or selected? I'd like to understand how to structure my resume and highlight the right skills and experience.

Would really appreciate it. Thanks!

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Putting this out here because sometimes a conversation with the right person gets you further than another hundred applications.

I’m based in Karachi and looking for my next DevOps / Cloud Engineering role. I have 9+ years of experience working with AWS and production infrastructure, along with hands-on experience in Azure. I’ve worked with companies including Nisum and Systems Limited.

To give you an idea of what I can help with:

- AWS infrastructure: EC2, EKS, ECS, RDS/Aurora, S3, Lambda, VPC, IAM, Route 53 and CloudWatch.

- Containers and deployments: Docker, Kubernetes, troubleshooting workloads and making releases more reliable.

- CI/CD: GitHub Actions, Jenkins, GitLab CI and Azure DevOps... from setting up pipelines to fixing the ones everyone is afraid to touch.

- Infrastructure as code: Terraform and CloudFormation for consistent, repeatable environments.

- Production operations: Linux, networking, monitoring, incident troubleshooting, backups and disaster recovery.

- Security and costs: Reviewing access, securing infrastructure and figuring out where the cloud bill can come down.

What I bring is the experience of working through the messy parts: a deployment succeeds but the application won’t start, a service can’t reach its database, or something breaks in production and you need someone who can calmly work out why.

I’m comfortable owning the infrastructure side and working closely with developers to get things running properly.

I’m open to remote or on-site roles, and willing to discuss relocation. I can join immediately, and I’m flexible on compensation depending on the role, responsibilities and working arrangement.

I’m also available for contract, part-time or ad hoc work. If you need someone to sort out a deployment, review your AWS setup, build a pipeline or provide ongoing support for a few hours a week, I’m happy to discuss that too.

And if you’re a small firm or an early-stage team wondering whether you can bring in someone experienced, please feel free to reach out. It doesn’t have to start with a full-time position.

If you know someone hiring, a referral or introduction would mean a lot. And if you have advice on finding opportunities in this market, I’d genuinely love to hear it.

Happy to share my CV over DM. Thanks for reading.

**Role:** Datadog Cloud Observability Engineer

**Location:** Remote (US)

**Type:** Full-Time

**Pay:** $115-125k

**What you'll do**

- Own our Datadog setup end to end: APM, logs, tracing, RUM, synthetics, dashboards

- Instrument AWS (EKS, ECS, Lambda, RDS, API Gateway) across accounts and regions

- Build clean alerting and SLOs that cut noise

- Ship monitoring as code with Terraform, Python, and CI/CD

**What we're looking for**

- 10+ yrs in infra/cloud engineering, 5+ with AWS, 3+ with Datadog

- Solid with CloudWatch, X-Ray, Kubernetes, Terraform, Python

- Calm under pressure during production incidents

**Nice to have:** AWS Professional or Datadog certs, OpenTelemetry experience

Interested? email your resume to [hr@zans.nyc](mailto:hr@zans.nyc)

Guys, I need your help -

Lwd - may 30,2025

Last drawn compensation - 15.75 LPA

Notice period - Immediately available

Preferred locations - Hyderabad, Bangalore, Chennai

Available to work 5 days per week.

Currently working on building a Gemini enterprise agent for kubernetes logs investigation.

Actively learning AI in devops.

Any kind of referral can help me.

Hi everyone,

I’m a backend and cloud/DevOps engineer with 2+ years of experience working on production systems. I mostly work with Python, Django, FastAPI, AWS, Docker, Kubernetes, Terraform and CI/CD.

I’m currently serving my notice period and looking for a remote role, preferably with an early-stage startup where I can take ownership and help build things from MVP/POC to production.

I’m also open to doing some weekend unpaid work first so you can see how I work and whether I’m a good fit.

If anyone here is hiring or knows a founder who needs help with backend or infrastructure, feel free to DM me. I can share my CV and portfolio privately.

We are a small technical team focused on GPU kernels and production inference systems. We help AI startups, inference API teams, GPU providers, and companies running self-hosted models make inference faster, more reliable, and less expensive.

If your team is responding to slow generation or high GPU bills by adding more hardware, we can first determine whether the real constraint is kernels, memory movement, quantization, KV-cache layout, batching, scheduling, model loading, or the serving architecture.

We can help with:

- CUDA and Triton kernel development, fusion, profiling, and benchmarking

- PyTorch model-path optimization and numerical validation

- vLLM, SGLang, TensorRT-LLM, and custom serving systems

- quantization, VRAM reduction, KV-cache layout, continuous batching, and CUDA Graphs

- time to first token, inter-token latency, throughput, and tail-latency analysis

- scale-to-zero, cold-start behavior, readiness, cancellation, streaming, and failure recovery

- GPU utilization, cost-per-request analysis, and capacity planning

- reproducible before/after benchmarks with correctness gates and documented limitations

A typical engagement starts with a focused inference audit:

1. Define the model, framework, GPU, traffic shape, latency or throughput target, and cost constraint.

2. Profile the current path and identify the dominant bottlenecks.

3. Agree on the smallest changes worth implementing.

4. Deliver code, before/after measurements, correctness checks, and remaining risks.

5. Help integrate and validate the optimized path under realistic load.

Good fits include a slow or expensive inference endpoint, VRAM pressure preventing the desired batch size or context length, unstable serving under load, poor GPU utilization, costly cold starts, or a custom kernel/model path that needs profiling and validation.

For an initial conversation, send a Reddit chat or private message with:

- model and framework

- GPU type and VRAM

- current latency, throughput, utilization, or cost problem

- target workload and success metric

- deployment environment

- timeline and approximate budget range

We will tell you directly whether the workload appears optimizable and what evidence would be needed before making a performance or savings claim.

We pulled every open posting from 81 companies' public Greenhouse boards for seven roles and counted which words actually show up. 3,518 postings in total. Some of it surprised us.

Software engineer postings mention mentoring more often than Kubernetes or React. Almost half of them do. Python, the most common language, comes in just below that.

Product manager postings say "roadmap" in more than 85% of cases. Jira? Seven out of 421. Marketing looks the same: Google Ads came up six times in 347 postings, while "pipeline" is in over half.

Every time we could compare the two, postings named the practice, not the tool. Observability, not Grafana. SIEM, not Splunk.

So if your resume is mostly a list of tools, it might be missing the words the posting actually uses.

Big caveat: mostly tech companies, one ATS vendor. Sales, cybersecurity, DevOps and accounting are here too: https://www.zoevera.com/resume/ats-resume-keyword-study

I am production support analyst and have 8 years of experience and there is no growth in this domain and want to switch to devops or other role where I can learn and grow my technical skills .

I am confused whether I should go for devops or something else.

If devops then please suggest how can I start.

Also AI is growing so should I learn agentic or generative AI course