Cloud / DevSecOps Engineer • AI / ML / GenAI Practitioner • 13+ yrs building infrastructure, automations & secure pipelines
I build resilient and secure infrastructure, automate pipelines, and I’m increasingly focused on integrating AI / ML / GenAI in a secure, reliable manner. I believe strong security + strong AI = powerful products. I enjoy solving complex infra problems, optimizing pipelines, and driving reliability.
- Designed & built CI/CD pipelines that reduced deployment time by ~60%
- Managed multi-cloud infrastructure handling 100K+ daily users
- Optimized infrastructure cost by 20–30% through right-sizing & automation
- Published n blog articles / projects on cloud & DevOps concepts
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Below are a few of my top projects — click to explore more details.
| 🔖 Name | Description | Highlights / Technologies |
|---|---|---|
| aws_terraform_jenkins_project | End-to-end IaC + CI/CD on AWS | Terraform, Jenkins, EC2, autoscaling, S3 |
| Terraforming-GCP-CloudBuild-GKE | Deploy Kubernetes via GCP pipeline | GCP Cloud Build, GKE, Terraform modules |
| aws-bootcamp-cruddur-2023 | Cloud / backend bootcamp project | Python APIs, AWS integration |
| DevOps-Project-Terraform-Jenkins-AWS | Full infrastructure & CI/CD lab | Terraform, Jenkins, AWS services |
Many more repos — see all
- GenAI / LLMOps: prompt engineering, deployment, safety guardrails
- Model Governance, Data Lineage, Auditing & Compliance in AI systems
- Adversarial ML, drift detection and secure inference
- Best practices in DevSecOps applied to AI/ML workloads
- Tools: MLflow, DVC, Weights & Biases, HuggingFace, Vertex AI / SageMaker, LangChain etc.
| Project | Description | What I Learnt / Implemented |
|---|---|---|
| GenAI-SecOps Dashboard | A small project that ingests security logs, uses LLM / genAI to generate insights & reports + alerts for anomalies | Built pipeline, deployed model, implemented access control, and automated scanning of incoming logs |
| MLModel Deployment Pipeline | Example ML model from data → training → container → deployment on cloud | Incorporated model versioning, CI/CD, Docker + Kubernetes, monitoring of model drift, cost optimization |
| Prompt Engineering Toolkit | Collection of prompts + workflows to fine-tune LLMs + test for injection / misuse, etc. | Learned to sanitise inputs, test for adversarial prompt examples, set rate limits etc. |
Cloud / Infra / DevSecOps: AWS · GCP · Terraform · Ansible · Kubernetes · CI/CD · Static Analysis · Vulnerability Scanning
AI / ML / GenAI: MLflow · DVC · HuggingFace · PyTorch / TensorFlow · Prompt Engineering · LLMs · LangChain · Bedrock / Vertex AI
Security Practices: Secure pipelines · Secrets management · Access control · Audits & compliance · Drift detection · Adversarial ML basics
(Optionally: top languages, streak stats, etc.)
- LinkedIn: in/mynameisameed
- Medium / Blog: msameeduddin.medium.com
- Open to: Collaborations, open source, DevOps / Cloud roles
