Delivering enterprise-grade AI solutions powered by reasoning, retrieval, and automation.

FEATURED PROJECTS
A curated selection of web and mobile projects combining UI/UX design, React development, smooth interactions, and modern, responsive performance-focused interfaces.
Personal developer portfolio showcasing web development skills, projects, and services. Buil...
A creative portfolio presented as an iOS-style phone interface, complete with simulated...
Live cryptocurrency dashboard showing real-time prices, trending coins, and the Fear &...

About Rishabh
I'm an AI/ML Engineer specializing in the architecture, design, and deployment of enterprise-scale AI solutions across Generative AI, Agentic AI, Computer Vision, and Machine Learning. My expertise lies in transforming complex business requirements into scalable, production-ready systems that deliver measurable impact.
I design end-to-end AI platforms by combining LLMs, multimodal intelligence, knowledge graphs, vector databases, and cloud-native infrastructure. From architecting advanced RAG and GraphRAG systems to building autonomous multi-agent workflows, I focus on creating reliable, secure, and high-performance solutions that operate effectively at scale.
Outside of engineering, I actively mentor aspiring AI professionals, contribute to AI communities, and explore emerging technologies shaping the future of intelligent systems.
Skills & Tech Stack
End-to-end intelligent systems — from classical ML and deep learning to agentic AI, cloud MLOps, and production deployment.
Services
From intelligent AI systems to full-stack applications — end-to-end engineering across the entire modern AI and software stack.
End-to-end ML solutions for real-world domains — supply chain, healthcare, IoT, and predictive analytics. From data pipelines to deployed models.
Building RAG systems, fine-tuning LLMs, and deploying intelligent search and document understanding solutions on Azure and GCP.
Autonomous multi-agent workflows using LangGraph, CrewAI, and Azure AI Foundry for intelligent orchestration and decision-making pipelines.
Scalable ML pipelines on Azure, AWS SageMaker, and GCP — covering model development, deployment, monitoring, and automation with MLflow and Docker.
Object detection, tracking, and video analytics pipelines using YOLO and deep learning models for infrastructure and real-time inference.
Text classification, NER, similarity search, and document parsing workflows. Turning unstructured content into structured, actionable insights.
Clean, modern web interfaces with attention to every visual detail. Responsive designs with smooth animations and polished aesthetics.
End-to-end web applications from backend APIs to frontend interfaces. Fast, scalable, and production-ready builds using modern frameworks.
CI/CD pipelines, containerization, and enterprise DevOps practices. Streamlining development workflows and deployment automation at scale.
Experience
From academics to enterprise AI — each stop on the map shaped who I am as an engineer and builder.
“Every milestone is a launchpad. Success is not where you stop — it's the relentless habit of never stopping.”
A clear, repeatable process: discover → design → build → refine — delivering clean UI, smooth interactions, and fast, responsive results.
Deep-dive into requirements, system constraints, and scalability goals. Define SLAs, data contracts, and edge cases before a line of code is written — aligning business needs with sound engineering decisions.
Design high-level and low-level system architecture — service boundaries, data flows, API contracts, and AI pipeline components. Every decision optimized for scalability, fault tolerance, and future extensibility.
Build production-grade systems with clean, modular, and testable code. From AI model integration to data pipelines and backend services — engineered with traceability and observability instrumented from day one.
Profile bottlenecks, tune latency and throughput, and enforce quality gates. Implement distributed tracing, structured logging, and metrics so every layer of the system is measurable, debuggable, and observable.
Automate delivery through CI/CD pipelines with staged rollouts and automated rollbacks. Monitor production with real-time dashboards, alerting, and SLO tracking — built to scale reliably on demand.
Certifications
Credentials earned across AI, cloud, and systems engineering — continuously expanding expertise across the full stack.
DeepLearning.AI · Coursera · Nov 2024
Amazon Web Services · Sep 2024
DeepLearning.AI · Coursera · Jul 2024
DeepLearning.AI · Mar 2025
Educative.io · Jan 2025
DeepLearning.AI · AWS · Dec 2024
DeepLearning.AI · Coursera · Nov 2024
Amazon Web Services · Sep 2024
DeepLearning.AI · Coursera · Jul 2024
DeepLearning.AI · Mar 2025
Educative.io · Jan 2025
DeepLearning.AI · AWS · Dec 2024
Blog
Insights on AI engineering, distributed systems, production architecture, and the craft of building software that scales.
From writing the bot logic to fixing ARM64 compatibility issues on Ubuntu — a practical...
Apr 2025
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Mar 2025
No laptop, no desktop — just an Android phone, a browser, and an obsession with...
Jan 2025
I'm Badhon Biswas (BadhonAI)—a Dhaka-based frontend & mobile developer and UI/UX designer. Let's create a fast, clean, SEO-friendly product with smooth interactions and modern UI.