Leela Kumar
Senior DevOps Engineer
I bring 8+ years of DevOps and platform engineering experience building and operating Kubernetes and cloud infrastructure at enterprise scale — designing AWS cloud abstractions, running multi-environment Kubernetes clusters, and leading CI/CD and GitOps modernization that cuts deployment time. Comfortable owning on-call and incident response, with hands-on experience building GPU-enabled Kubernetes infrastructure (NVIDIA GPU Operator, EKS, Triton, NIM) for AI/ML workloads.
A little about me
Systems work rewards patience and pattern recognition — so does most of what I do outside of it. I've been playing chess since I was a kid, still play in local table tennis and volleyball leagues, and I'm working toward training five days a week. None of it pays the on-call pager, but it keeps the problem-solving muscle in shape.
Certifications
Kubernetes, cloud, and NVIDIA AI infrastructure credentials, kept current. View all badges on Credly →
Workshops
Workshops and sessions I've attended, covering Kubernetes, GPU infrastructure, and AI platform engineering.
Paradise Valley, AZ
Red Hat Summit: Connect — Paradise Valley
Full-day regional Red Hat event at Sanctuary Camelback Mountain covering OpenShift Kubernetes deployments on AWS, Ansible Tower, and Ansible automation workflows for building an internal developer platform. Closed out with a 3.5-hour hands-on lab applying the concepts end-to-end.
Virtual
NVIDIA AI Infrastructure Professional — Instructor-Led Workshop
Five-day, full-day virtual instructor-led workshop covering enterprise AI infrastructure, GPU-accelerated computing, and production-ready AI platforms — led by NVIDIA-certified instructor Bill Ferguson. Part of an ongoing track deepening NVIDIA AI infrastructure and platform engineering expertise.
Austin, TX
Google Cloud Labs: AI Toolkit Workshop
Full-day, in-person hands-on workshop at the Google office in Austin, hosted by GDG Austin — Cloud Engineers, with breakfast, lunch, and dinner provided. Explored how Gemini AI-powered agents assist with Kubernetes troubleshooting, log analysis, root cause analysis, cluster monitoring, and AI-assisted incident response for cloud-native operations.
Austin, TX
Austin vLLM & llm-d AI Inference Meetup
Technical meetup hosted by Red Hat AI, NVIDIA, and the AITX Community, diving into the architecture and engineering behind vLLM and llm-d inferencing. Included a hands-on workshop segment on model compression and benchmarking, plus sessions on distributed inference with Kubernetes and scaling LLM inference in production.
Webinars
Live online sessions I've attended on AI infrastructure, MLOps, and platform engineering.
Virtual
Agentic ML in Action: Train, Deploy & Monitor ML Models with Snowflake CoCo
Live Snowflake webinar on agentic ML across the full model lifecycle — develop, orchestrate, manage, deploy, and monitor under unified governance. Covered Cortex Code (CoCo) agent skills, AutoML from a single natural-language prompt, model registry and lineage, batch and real-time inference, drift monitoring, and the container runtime running PyTorch, XGBoost, and Ray on GPU/CPU compute.
Projects
Repositories from my GitLab — infrastructure-as-code, GPU/AI platforms, and a couple of side builds. Click any card to open the repo.
Terraform-defined AWS foundation — VPC, networking, and the account baseline shared across environments.
Kubernetes manifests and Helm charts for deploying and managing application workloads.
An AI-assisted Terraform workflow experiment — natural-language prompts driving infrastructure-as-code changes.
Early-stage desktop app scaffolding for running and interacting with AI models locally.
AWS-based Slurm HPC cluster — CPU/GPU partitions, Munge auth, and NVIDIA Base Command Manager.
CUDA-accelerated GPU compute workloads and benchmarking.
TensorFlow inference service with GPU scheduling, node taints, and tolerations.
PyTorch inference service with CUDA acceleration on GPU-scheduled nodes.
GPU utilization monitoring using nvidia-smi dmon alongside Prometheus and Grafana.
NVIDIA Triton Inference Server platform serving containerized Llama 3 and custom models.
End-to-end AI chat application on Kubernetes with Istio ingress, autoscaling, and observability.
NVIDIA NIM microservices platform for containerized model inference.
GPU-enabled AWS EKS infrastructure via Terraform, Helm, and the NVIDIA GPU Operator.
GPU discovery and scheduling — Device Plugin, GPU Feature Discovery, and Container Toolkit.
GPU-backed inference microservice with autoscaling and health checks.
Let's Connect
Open to Senior/Staff Platform, DevOps, and GPU/AI Infrastructure roles — remote or Denver-area. Pick whichever's easiest.
Email Best for detailed questions lkdevops27@gmail.com LinkedIn Let's connect professionally View profileSend a message
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