Freelancers using Helm

Freelancers using HelmFreelancers using Helm

Projects People

Chidozie Uzoegwu

Newcastle upon Tyne, UK

Data/ML Engineer | AWS & Terraform | LLM/RAG & MLOps

Data/ML Engineer | AWS & Terraform | LLM/RAG & MLOps

Cover image for PostgreSQL Deployment on AWS EKS for NHS

PostgreSQL Deployment on AWS EKS for NHS

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Cover image for Cardiovascular Studies RAG System Development

Cardiovascular Studies RAG System Development

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Cover image for Full-Stack To-Do App Development with React and AWS Amplify

Full-Stack To-Do App Development with React and AWS Amplify

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Cover image for React Setup in Vite with HMR

React Setup in Vite with HMR

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Ramat Gan, Israel

DevOps, GCP, AWC, PCC, Docker, K8s, Terraform, CI/CDPipeline

8: Followers

DevOps, GCP, AWC, PCC, Docker, K8s, Terraform, CI/CDPipeline

Cover image for High-Security DevOps Pipeline for a Financial Platform

High-Security DevOps Pipeline for a Financial Platform

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Cover image for Scalable Cloud Migration for a FinTech Platform

Scalable Cloud Migration for a FinTech Platform

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Cover image for Multi-Region Disaster Recovery & High Availability Setup

Multi-Region Disaster Recovery & High Availability Setup

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Senior DevOps/SRE | AWS, Kubernetes & Terraform Expert

New to Contra

Senior DevOps/SRE | AWS, Kubernetes & Terraform Expert

Cover image for Architected and implemented a production-grade,

Architected and implemented a production-grade, fully automated DevOps platform on AWS, covering the entire software delivery lifecycle from code commit to live deployment. The platform integrates eight tightly coupled layers: a developer workflow built on Git, Docker Compose, and pull requests; a GitHub Actions CI/CD pipeline with dedicated workflows for continuous integration, deployment, Terraform provisioning, security scanning, and semantic releases; and a multi-tool security gate enforcing SAST, SCA, IaC scanning, container analysis, OWASP ZAP, and CodeQL checks before any artifact progresses. Infrastructure is provisioned as code using Terraform, deploying a multi-AZ VPC, an EKS cluster, ECR repositories, RDS MySQL, an ALB with WAF, and IAM OIDC integration on AWS. Applications are packaged with Helm Umbrella Charts and deployed to Kubernetes with Horizontal Pod Autoscaling (2–8 replicas) and RBAC-enforced NetworkPolicies. GitOps state management is handled by ArgoCD, providing continuous reconciliation, drift detection, auto-sync, and self-healing across all application sets. Observability is delivered through a dual-stack setup: a metrics pipeline (Prometheus → Grafana → Alertmanager → Slack) and a log aggregation pipeline (Filebeat → Logstash → Elasticsearch → Kibana). Releases are fully automated using Semantic Release with Conventional Commits, producing versioned GitHub Releases and Docker image tags without manual intervention.

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Cover image for Comparing AWS ECS and AWS EKS for Container Orchestration

Comparing AWS ECS and AWS EKS for Container Orchestration

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Cover image for Implementing Additional Checks for AWS Security

Implementing Additional Checks for AWS Security

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Cover image for Designed and developed a Command

Designed and developed a Command Line Interface (CLI) security tool that leverages the Google Gemini API (gemini-2.5-flash) to perform advanced, AI-driven vulnerability analysis on Python codebases. This tool acts as an automated security engineer, scanning source code to detect critical flaws before they reach production. The scanner parses Python files and evaluates them against a detailed security prompt to identify vulnerabilities such as SQL injection, hardcoded secrets (API keys and database passwords), weak cryptography (e.g., MD5 hashing), insecure deserialization, and improper input validation. It outputs a structured, color-coded terminal report assigning severity ratings (🔴 High, 🟠 Medium, 🟢 Low) to each finding. For every detected issue, the tool provides the vulnerability type, the exact line number, a concise explanation of the risk and potential impact, and actionable remediation advice with secure code fixes. The architecture is built on a lightweight Python foundation using a virtual environment for dependency isolation. It utilizes the google-generativeai SDK for model interaction and python-dotenv for secure environment variable management, ensuring API keys are never hardcoded. This project demonstrates the ability to integrate cutting-edge Large Language Models (LLMs) into traditional DevSecOps workflows, providing enterprise-grade security scanning capabilities directly within the developer's terminal.

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Yasser Boukhers

CI/CD Specialist | Kubernetes, Docker & Jenkins Guru

$1k+: Earned

1x: Hired

3: Followers

CI/CD Specialist | Kubernetes, Docker & Jenkins Guru

Cover image for High Availability Jenkins CloudBees Deployment Automation

High Availability Jenkins CloudBees Deployment Automation

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Cover image for Scalable Kubernetes Infra with Automated Canary Deployment

Scalable Kubernetes Infra with Automated Canary Deployment

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Cover image for Infrastructure Automation with Advanced Ansible Techniques

Infrastructure Automation with Advanced Ansible Techniques

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Stanislav Stankov

Sofia, Bulgaria

Principal Product Manager

Principal Product Manager

Cover image for SaaS Shipment Tracking System

SaaS Shipment Tracking System

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Cover image for AOL Communities PoC

AOL Communities PoC

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5

Cover image for PLG Data Driven Growth for B2B SaaS

PLG Data Driven Growth for B2B SaaS

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3

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Shereen Hasnain

Lahore, Pakistan

DevOps Engineer | Docker, Kubernetes & CI/CD Specialist.

New to Contra

DevOps Engineer | Docker, Kubernetes & CI/CD Specialist.

Cover image for 🚀 Just completed a Kubernetes

🚀 Just completed a Kubernetes Logging project using the ELK Stack! Built and verified a centralized logging pipeline using: 🔹 Kubernetes & Minikube 🔹 Helm 🔹 Filebeat 🔹 Elasticsearch 🔹 Kibana Kubernetes logs are now collected by Filebeat, stored in Elasticsearch, and visualized through Kibana Discover. This project strengthened my hands-on skills in Kubernetes logging, Helm deployments, troubleshooting, and observability.

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Cover image for 🚀 Completed a Kubernetes Monitoring

🚀 Completed a Kubernetes Monitoring Project with Prometheus & Grafana As part of my DevOps learning journey, I completed a hands-on Kubernetes monitoring project where I deployed and monitored a Kubernetes environment using Helm, Prometheus, and Grafana. 🔧 Technologies Used: • Kubernetes & Minikube • Helm • Prometheus • Grafana • Node Exporter • Kube State Metrics 📌 What I implemented: • Deployed Prometheus and Grafana using Helm • Monitored Kubernetes pods and services • Collected node and cluster metrics • Created a Grafana monitoring dashboard • Monitored CPU, memory, and pod status • Created PromQL queries for monitoring • Configured a High CPU Usage alert (CPU > 80%) • Verified deployments using kubectl get pods, kubectl get svc, and helm list 📸 Screenshots below show the actual implementation, including Kubernetes resources, Helm releases, Grafana dashboard, and alert configuration. This project helped me strengthen my understanding of Kubernetes monitoring, observability, Prometheus, Grafana, Helm, and alerting Currently continuing my journey toward becoming a Junior DevOps Engineer, focusing on Kubernetes, CI/CD, cloud technologies, monitoring, and automation. hashtag#DevOps (https://www.linkedin.com/search/results/all/?keywords=%23devops&origin=HASH_TAG_FROM_FEED) hashtag#Kubernetes (https://www.linkedin.com/search/results/all/?keywords=%23kubernetes&origin=HASH_TAG_FROM_FEED) hashtag#Prometheus (https://www.linkedin.com/search/results/all/?keywords=%23prometheus&origin=HASH_TAG_FROM_FEED) hashtag#Grafana (https://www.linkedin.com/search/results/all/?keywords=%23grafana&origin=HASH_TAG_FROM_FEED) hashtag#Helm (https://www.linkedin.com/search/results/all/?keywords=%23helm&origin=HASH_TAG_FROM_FEED) hashtag#Monitoring (https://www.linkedin.com/search/results/all/?keywords=%23monitoring&origin=HASH_TAG_FROM_FEED) hashtag#Minikube (https://www.linkedin.com/search/results/all/?keywords=%23minikube&origin=HASH_TAG_FROM_FEED) hashtag#CloudComputing (https://www.linkedin.com/search/results/all/?keywords=%23cloudcomputing&origin=HASH_TAG_FROM_FEED) hashtag#DevOpsJourney (https://www.linkedin.com/search/results/all/?keywords=%23devopsjourney&origin=HASH_TAG_FROM_FEED)

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Cover image for 🚀 Ansible Nginx Automation Project
Built

🚀 Ansible Nginx Automation Project Built a hands-on DevOps project to automate Nginx deployment and configuration using Ansible. 🔧 Key concepts: • Ansible Roles & Playbooks • Jinja2 Templates • Handlers & Tasks • Nginx Configuration • Linux Server Automation • Infrastructure as Code (IaC) This project strengthened my practical understanding of server automation and configuration management.

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Cover image for CVision AI | AI-Powered Resume

CVision AI | AI-Powered Resume Analyzer Built, containerized, and deployed an end-to-end AI Resume Analyzer. Analyzes resumes for ATS scoring, job matching, and skill gap suggestions. Tech Stack: Docker, Git/CI/CD, Render Cloud, Python/Flask, OpenRouter AI

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AI Output Evaluator & LLM Quality Specialists

New to Contra

AI Output Evaluator & LLM Quality Specialists

Cover image for A sample LLM evaluation report

A sample LLM evaluation report analyzing AI responses for accuracy, reasoning quality, hallucinations, and failure patterns. Demonstrates structured testing and quality assessment workflows.

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Cover image for Evaluated 60 multi-turn conversations from

Evaluated 60 multi-turn conversations from a retail support AI to track context retention, persona consistency, and task completion across longer exchanges. Found that context drops clustered around the third and fourth turn, pointing to a memory window limitation rather than random failures, and flagged each instance with the exact turn where context broke down.

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Cover image for Ran 48 adversarial prompts against

Ran 48 adversarial prompts against a production LLM to surface jailbreak vulnerabilities, role override attempts, and context manipulation before a public launch. Found 11 vulnerabilities including 3 critical, documented every failure with reproducible prompts, and delivered a prioritised risk report with fixes.

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Cover image for Built a custom evaluation rubric

Built a custom evaluation rubric for scoring AI customer support outputs across five weighted dimensions: factual accuracy, reasoning validity, instruction adherence, adversarial robustness, and format compliance. Each dimension includes plain-language scoring criteria so any team member can apply it consistently, no AI background required.

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São Paulo, Brazil

DevOps engineer fixing CI/CD and cloud failures

DevOps engineer fixing CI/CD and cloud failures

Cover image for Docker & Kubernetes Issues

Docker & Kubernetes Issues

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Cover image for AWS Deployment Fix

AWS Deployment Fix

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Cover image for Terraform Debug

Terraform Debug

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Cover image for CI/CD Pipeline Failure Root Cause

CI/CD Pipeline Failure Root Cause & Fix

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