Shaik Afzal Hussain · AWS & AI Engineer

About Me

Building reliable, scalable cloud systems and shaping responsible AI.

I am a Cloud & AI Engineer and ML Data professional at Amazon Web Services (AWS), where I work on AWS Bedrock to train, evaluate, and improve Large Language Models (LLMs). I operate end-to-end on multi‑modal datasets text, speech, audio, image, and video focusing on data quality analysis, error‑pattern investigation, and stronger model evaluation workflows. I build and maintain technical pipelines and in‑house tooling that make model development reproducible and production‑ready.

Previously I worked as an AWS Cloud Application Developer designing and deploying secure, multi‑AZ, auto‑scaling architectures using EC2, ELB, IAM, EBS, S3 and Auto Scaling, and automating cloud operations with Bash and Python. I also design CI/CD and DevOps workflows (Docker, Kubernetes, Terraform, Jenkins, SonarQube, DockerHub) that deliver reliable releases for cloud and AI workloads.

Selected outcomes include delivering 99.99% uptime and a 45% reduction in latency across production services. I’ve built high‑availability AWS apps, scalable 3‑tier architectures, and end‑to‑end DevOps CI/CD pipelines, and contributed to projects such as CoCreate.AI, Study Buddy AI, and the ATS Resume Scanner.

Hands-on with Amazon Q and Amazon Nova foundation models.
Built secure, multi‑AZ, auto‑scaling cloud architectures on AWS.
Results‑driven: improved uptime to 99.99% and reduced latency by 45%.
Google Arcade Cohort 2 — completed hands‑on labs, quests, and cloud skill challenges on GCP.

Professional Experience

Total experience: —

ML Data Associate, AWS Bedrock

Amazon Web Services (AWS) · 11/2024 – Present · Bengaluru, India —

Train and evaluate LLMs via AWS Bedrock; exposure to Amazon Q and Amazon Nova.
Deliver high-quality labeled datasets across text, speech, audio, image, and video.
Analyze error patterns, propose fixes, and improve data quality and processes.
Operate in-house tooling; interpret and implement detailed technical instructions.

AWS Cloud Application Developer

Rooman Technologies Pvt Ltd · 02/2024 – 10/2024 · Kurnool, India —

Designed and deployed infrastructure with EC2, Auto Scaling, ELB, IAM, and EBS.
Automated ops and admin tasks using scripts, improving efficiency.
Worked across Linux, shell scripting, databases, and data management.

Technical Skills

Cloud

AWS: EC2, ELB, EBS, S3, IAM, Auto Scaling, CloudWatch, SNS, SQS, RDS

GCP: Compute Engine, Cloud Storage, Cloud SQL, Cloud Monitoring, Pub/Sub, IAM, Cloud DNS, Cloud Functions, GKE

DevOps

Git, GitHub, Docker, Kubernetes, Jenkins , Terraform

Scripting

Bash, Python (basic)

Artificial Intelligence (AI)

Language Models (LLM) , Agents , Natural Language Processing (NLP) , Automation , AWS Bedrock Azure AI, Google Vertex AI , Prompt Engineering, Retrieval-Augmented Generation (RAG), Model Evaluation, Fine-tuning, Vector Databases & Embeddings , LangChain

Selected Projects

ATS Resume Scanner

Free GCP-hosted tool to analyze resume ATS-fit, keyword gaps, and feedback.

Docker + Artifact Registry + Cloud Run
~30s analysis · ~$0.50–$1.00/month via free tiers

Live App

High Availability Web App on AWS

Highly available deployment leveraging Auto Scaling, ELB, Multi-AZ.

99.99% uptime · 45% faster loads · +30% responsiveness
+50% concurrent users during peaks

Source

3‑Tier Cloud App on AWS

Scalable 3‑tier architecture in AWS with Auto Scaling and secure IAM.

+40% capacity scaling without manual intervention
99.9% uptime via Multi‑AZ + Auto Scaling

Source

End-to-End DevOps Project — Flask App CI/CD Pipeline

A complete end-to-end DevOps lifecycle project demonstrating CI/CD for a Flask application using Terraform, Jenkins, SonarQube, Docker, DockerHub, and AWS EC2.

Automated build and test pipeline in Jenkins
Static analysis with SonarQube
Docker image build & push to DockerHub
Automatic deployment to AWS EC2
Infrastructure provisioning using Terraform

Architecture Flow: GitHub → Jenkins → SonarQube → DockerHub → AWS EC2

GitHub

CoCreate.AI — Human + AI Co-Creation Platform

An AI-powered web platform built for Vibeathon 2025 that improves human-AI collaboration using prompt refinement, story mode, idea mode, tutor mode, and more.