Syed Kashif Ali

Cloud DevOps & Forward Deployed Engineer

Hyderabad, Telangana, India

kashifsayyad09@gmail.com

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About

I'm a Cloud DevOps and Forward Deployed Engineer passionate about building scalable cloud infrastructure, automating deployments, and solving real-world engineering challenges. I work with AWS, multi-cloud technologies, Docker, Kubernetes, Terraform, CI/CD pipelines, and cloud-native architectures. I'm also exploring Generative AI, AI agents, and Model Context Protocol (MCP) to build intelligent, production-ready solutions. I enjoy participating in developer communities, hackathons, and technical events where I can collaborate, learn, and turn innovative ideas into practical projects. Always excited to connect with developers, cloud engineers, AI enthusiasts, and people building the future of technology.

Skills

aws

azure

devops

LLMops

LangChain

CI/CD

System Design

Google Cloud

Experience

DevOps Engineer - Intership

Cyser IT Services

Jun 2026 to Present

Working on cloud-native application deployment and DevOps automation using AWS and Linux.
Building and maintaining CI/CD pipelines using GitHub Actions.
Containerizing applications using Docker and Docker Compose.
Deploying and managing containerized workloads using Kubernetes, including deployments, services, ingress, and autoscaling.
Working with AWS EC2, RDS MySQL, and cloud networking for application deployment.
Configuring Nginx reverse proxy and troubleshooting frontend-backend connectivity.
Implementing observability using Prometheus, Grafana, CloudWatch, and application health checks.
Monitoring application metrics, analyzing logs, and troubleshooting deployment and performance issues.
Working with Python MCP servers and AI integrations to connect applications with intelligent tools.
Managing environment variables, application configurations, and deployment troubleshooting.

Cloud Engineer - Internship

Future Application

Nov 2025 to Dec 2026

Worked on cloud computing and DevOps practices, gaining practical experience with application deployment, infrastructure automation, and CI/CD workflows. Explored containerization using Docker, cloud infrastructure on AWS, and automation tools to improve deployment consistency and application reliability. Collaborated on technical tasks, troubleshooting, and learning modern cloud-native development practices.

Projects

AMD-vLLM-kubernetes

Built an educational deployment workflow for running AI inference workloads on Kubernetes clusters powered by AMD GPUs. Automated cluster setup, container runtime configuration, Calico networking, AMD GPU Operator installation, and vLLM inference deployment through shell scripts. Configured a Kubernetes LoadBalancer service using MetalLB to expose the inference API for testing. The project explores GPU-accelerated AI serving, container orchestration, and cloud-native infrastructure automation.

Kubernetes

vLLM

AMD GPU Operator

AMD Instinct GPUs

Docker/containerd

Calico CNI

Helm

MetalLB

Linux

Bash scripting

Llama 3.2 1B

Employer-AI

Employer-AI is an AI-powered recruitment platform designed to streamline hiring by helping employers manage recruitment workflows, evaluate candidates, and improve hiring efficiency through intelligent automation.

Python

AI/LLMs

React

Node.js

REST APIs

AWS

MySQL

cyptred-ai

Developed AI Exam Guardian, a privacy-first online exam integrity platform that correlates browser activity and behavioral signals to identify potentially suspicious exam-session patterns. Built a layered architecture using a React frontend, Python FastAPI backend, Model Context Protocol (MCP) server, a Retrieval-Augmented Generation (RAG) system, and an AI gateway with Groq and NVIDIA fallback. Includes deterministic risk scoring, evidence timelines, policy-grounded incident explanations, and a teacher dashboard for human review.

Python

FastAPI

React

TypeScript

Model Context Protocol (MCP)

RAG

Groq

NVIDIA NIM

MySQL

AWS

Terraform

Docker

Docker Compose

Nginx

GitHub Actions

Amazon ECR

Prometheus

Grafana

waste-flow-ai

Built WasteFlow AI, a smart waste collection prioritization platform that helps collection teams identify which bins need attention first. Developed a deterministic, explainable priority engine that scores bins from 0–100 based on fill level, estimated overflow time, days since collection, waste type, and temperature. Implemented a ranked collection plan with transparent reasons for each priority, a React dashboard, a Python FastAPI backend, and AWS RDS MySQL integration for bin data and collection history.

React

Vite

Python

FastAPI

Model Context Protocol (MCP)

MCP Server

SQLAlchemy

PyMySQL

AWS RDS MySQL

MySQL

Nginx

REST APIs

pytest

Ecommerce-microservices-app

Developed and deployed an e-commerce microservices application using AWS EKS and Kubernetes. Configured infrastructure with Terraform, managed application deployment through Argo CD and GitOps workflows, and used Nginx Ingress for traffic routing. Implemented centralized logging with the EFK stack and monitoring with Prometheus and Grafana, with AWS RDS for database persistence. The project demonstrates container orchestration, infrastructure as code, automated deployment, observability, and cloud-native application management.

Python

Flask

Docker

Kubernetes

Amazon EKS

AWS RDS

Terraform

Argo CD

GitOps

Nginx Ingress

Prometheus

Grafana

Elasticsearch

Fluent Bit

Kibana

GitHub Actions

SQL

BankingOps – AI-Powered Banking Platform

Developed BankingOps, an AI-powered banking platform that combines banking application workflows with an intelligent conversational chatbot. The project focuses on improving user interactions through natural language conversations while exploring modern application architecture, backend integration, and DevOps automation. It brings together banking operations and AI-driven assistance in a unified platform.

Add the technologies implemented in your project

such as Python

Flask

AI/LLMs

REST APIs

Docker

Kubernetes

AWS

Terraform

and CI/CD.

Education

Bachelor of Computer Applications (BCA)