Headline

Building Scalable Cloud Infrastructure & AI-Powered DevOps Tooling | AWS • GCP • Docker • Jenkins • Ansible • ELK • Grafana • Gen AI • Kubernetes • Terraform | Open to Opportunities

About

For the past 3 years, I’ve worked across AWS, GCP, and Azure building production infrastructure that had to stay online while teams shipped fast. Most of the work people notice is deployment speed. The work that matters usually starts at 2 AM when something fails and everyone wants answers in ten minutes. At Block-Stars in Seoul, I worked on the DevOps side of platforms like Aslan and Kingpot — multiplayer gaming and casino systems handling real-money transactions where downtime wasn’t a dashboard metric. Players noticed immediately. So did the business. My day-to-day work looked like this: → Managing Kubernetes environments on EKS and GKE for containerised microservices → Building Jenkins and GitLab CI/CD pipelines that turned multi-hour deployments into releases that finished in minutes → Automating infrastructure provisioning with Terraform, cutting repetitive setup work by more than 70% → Running observability stacks with Prometheus, Grafana, ELK, Datadog, and Zabbix so engineers saw problems before users did → Hardening infrastructure with IAM controls, Cloudflare WAF rules, SSH restrictions, and production access policies One production issue still stays with me. A MongoDB CPU spike hit one of our gaming systems during peak traffic hours and started slowing API responses across multiple services. We traced it to inefficient queries and replication lag, failed over traffic, tuned the queries, and stabilised the cluster before the outage spread further. The fix took hours. The RCA took longer. That’s usually the real work in DevOps — not just restoring systems, but making sure the same failure doesn’t return three weeks later wearing a different name. I’m now working at NIDEC Advance Technology on enterprise infrastructure and automation across multi-cloud environments. Right now, the part that interests me most is AI-integrated DevOps — using LLMs and intelligent automation in places where they actually reduce operational load instead of adding another layer of complexity people have to maintain later. A lot of companies say they want engineers who can “move fast.” Most actually need engineers who can keep systems stable while the company moves fast around them. Those are different skills. 📧 manjunathhuddar1999@gmail.com 🔗 linkedin.com/in/manjunath-huddar-devops

Location

Bengaluru

AI stack

antigravity

claude code

claude

codex

github copilot

ollama

Work experience

Software Engineer at NIDEC Advance Technology India, Jul 2025 - Present · 1 yr 3 mos, Bengaluru, Karnataka, India

DevOps Engineer at Block-Stars Pvt. Ltd., Jul 2023 - Jul 2025 · 2 y, Seoul, South Korea, Built a GenAI Dockerfile Generator using LLaMA3 and Ollama takes a project description as input and generates production-ready Dockerfiles, reducing developer onboarding time and eliminating manual container config errors.

Education

Bachelor of Engineering - BE, Information Science and Engineering, Basaveshwar Engineering College (A), Bagalkote

Nano Degree, Cloud Computing, PrepInsta

Public links

github: https://github.com/Shubham070msd

manjunath-07.vercel.app: https://manjunath-07.vercel.app/