I'm Akil Mahmod Tipu — an engineer focused on AI systems, cloud platforms, and backend architecture. My background in AWS, Kubernetes, and infrastructure automation means I bring production discipline to everything I build. Currently working on LLM applications, platform engineering, and distributed systems.
From AI-powered applications to cloud infrastructure — engineering with reliability, architecture, and production readiness at the core.
LLM applications, RAG pipelines, agent architectures, and the evaluation frameworks to keep them reliable.
AWS architecture, Kubernetes orchestration, Terraform automation, and infrastructure designed for scale.
APIs, distributed systems, data pipelines, and services built for throughput and reliability.
CI/CD pipelines, observability, deployment strategies, and the operational discipline to run systems well.
Real systems built with real constraints — architecture, trade-offs, and production concerns.
Production-ready deployment workflow for microservices on Amazon EKS. Covers Terraform-managed infrastructure, Docker containerization, Kubernetes orchestration, and a scalable cluster architecture designed for real workloads.
View repositoryComplete CI/CD pipeline for a Java application with integrated security scanning (Trivy, SonarQube), artifact management (Nexus, ECR), and automated deployment to Kubernetes — demonstrating DevSecOps pipeline architecture.
View repositoryContinuous integration and deployment pipeline utilizing Jenkins, Maven, AWS ECR, and Docker Compose. Designed for streamlined software delivery with automated build, test, and deployment stages.
View repositoryThe problems I'm actively working on and the areas I'm going deeper in.
LLM-Powered Applications
Building production RAG systems and exploring agent architectures for real-world use cases.
Platform Engineering
Designing developer platforms and internal tooling that reduce cognitive load and increase velocity.
AI Infrastructure
Solving deployment, observability, and cost challenges for AI workloads at scale.
Distributed Systems
Going deeper on event-driven architecture, consistency trade-offs, and resilience patterns.
System Design
Practicing end-to-end system design with production constraints — latency, cost, reliability.
Technical Writing
Publishing deep dives and architecture analysis on AI engineering and cloud platforms.
Deep dives on AI engineering, cloud architecture, backend systems, and production lessons.
A preview of the advanced technical content coming soon — covering AI/ML systems design, cloud-native platform engineering, infrastructure as code at scale, and real-world architecture patterns.
