AI Research & Development
Discover the power of cutting-edge artificial intelligence research tailored for next-generation applications, autonomous systems, and intelligent solutions
Agentic AI Systems
Discover autonomous intelligence that thinks, plans, and acts independently to solve complex real-world challenges
Agentic AI Systems
Autonomous Intelligence That Thinks, Plans & Acts
RAG Architecture
Intelligent knowledge retrieval that enhances LLM capabilities with real-time, contextual information access
RAG Architecture Systems
Intelligent Knowledge Retrieval That Enhances LLM Capabilities
Multi-Modal AI Systems
Unified intelligence that seamlessly integrates vision, language, and sensory data for comprehensive understanding and interaction
Multi-Modal AI Systems
Unified Intelligence That Integrates Vision, Language & Sensory Data
Shayan Alahyari
AI Researcher
Master's student at Western University specializing in production-ready AI systems. Building LLM-powered applications and agentic AI platforms that bridge cutting-edge research with real-world impact.
Why This Roadmap
With LLMs and agentic AI transforming industries, the demand for production-ready AI systems is exploding. I created this comprehensive roadmap to bridge the gap between theory and implementation.
Complete Learning Path
From foundational concepts to advanced deployment strategies, explore structured resources that take you from AI basics to building scalable, production-ready systems.
Education
Computer Science
Supervisor: Prof. Mike Domaratzki
Computer Science
Research Publications
Research Projects
Processed 2TB+ genomic datasets with 500+ BAM files across 20+ chromosomes
7-step pipeline: BAM indexing, filtering, base recalibration, variant calling
Optimized parallel computing workflows improving efficiency by 30%
SNP filtering and GenomicsDB integration for ML-ready genomic data
Experience
- Developing agentic AI platform connecting graduate students with professors using LLM-powered matching algorithms and RAG-based research retrieval
- Architecting end-to-end ML pipeline with LLM APIs and RAG architecture for semantic analysis of research profiles and academic publications to generate personalized recommendations
- Building multi-dimensional ranking algorithms leveraging vector embeddings and similarity search to evaluate research alignment, publication metrics, and mentoring compatibility
- Developed shell and Python scripts to process large genomic datasets for machine learning
- Managed and processed over 2TB of genomic data, handling 500+ BAM files across 20+ chromosomes
- Conducted BAM indexing, filtering, base recalibration, variant calling, and SNP filtering
- Improved processing efficiency by 30% through optimized parallel computing on servers
- Utilized technologies including GATK, Miniconda, samtools, parallel, Java, and Python
Technical Skills
Honors & Awards
Education
Computer Science
Supervisor: Prof. Mike Domaratzki
Computer Science
Experience
- Developing agentic AI platform connecting graduate students with professors using LLM-powered matching algorithms and RAG-based research retrieval
- Architecting end-to-end ML pipeline with LLM APIs and RAG architecture for semantic analysis of research profiles and academic publications to generate personalized recommendations
- Building multi-dimensional ranking algorithms leveraging vector embeddings and similarity search to evaluate research alignment, publication metrics, and mentoring compatibility
- Developed shell and Python scripts to process large genomic datasets for machine learning
- Managed and processed over 2TB of genomic data, handling 500+ BAM files across 20+ chromosomes
- Conducted BAM indexing, filtering, base recalibration, variant calling, and SNP filtering
- Improved processing efficiency by 30% through optimized parallel computing on servers
- Utilized technologies including GATK, Miniconda, samtools, parallel, Java, and Python
Technical Skills
Honors & Awards
AI Agents
Build intelligent agents that think, plan, and act independently
AI agents are systems capable of autonomously performing tasks on behalf of users by designing workflows and utilizing available tools
Built on LLMs, they go beyond traditional models by using tool calling to obtain real-time information and create subtasks autonomously
Core capabilities include perception, reasoning, memory, planning, and learning through feedback mechanisms
They excel at goal decomposition, breaking complex objectives into manageable subtasks without human intervention
Three-level progression: LLMs → AI Workflows → AI Agents, explained for non-technical users
Key insight: AI agents replace human decision-making with LLM autonomous reasoning and action
Covers ReAct framework (Reason + Act) and iterative improvement capabilities of modern agents
Real-world examples including calendar integration, content creation workflows, and video analysis agents
Comprehensive 11-lesson course covering AI agent fundamentals to production deployment
Hands-on learning with Python code samples, video tutorials, and practical exercises using Microsoft technologies
Advanced topics include agentic design patterns, trustworthy AI, multi-agent systems, and metacognition patterns
Production-ready examples using Azure AI Agent Service, Semantic Kernel, and AutoGen frameworks
Most extensive collection of GenAI agent tutorials covering research, content creation, shopping analysis, and productivity agents
Ranges from basic conversational bots to complex multi-agent systems with sophisticated controllable agents for complex RAG tasks
Includes specialized agents for systematic reviews, grocery management, quality assurance testing, and LangGraph system inspection
Features advanced techniques like paper draft creation, task allocation, and EU regulatory compliance bots with 20k+ newsletter subscribers
Complete journey from beginner to expert in understanding, using and building AI agents with free certification available
Hands-on learning with Hugging Face Spaces, interactive quizzes, live sessions, and practical assignments including building your first agent "Alfred"
4-unit comprehensive curriculum: Agent Fundamentals, Popular Frameworks (smolagents/LlamaIndex/LangGraph), Real-world Use Cases, and Final Project
Community-driven course with Discord study groups, student competitions, leaderboards, and bonus units including Pokemon battles and LLM fine-tuning
Comprehensive primer for leaders on Agentic AI that acts on data rather than just analyzing it
Hands-on experience building AI agents using Custom GPTs with real assignments including human-in-the-loop systems and practical business applications
Learn to differentiate innovation from hype, understand flipped interaction patterns, multimodal capabilities, and ahead-of-time planning strategies
Covers tool descriptions, agent feedback loops, in-context learning, and Retrieval Augmented Generation with shareable LinkedIn certificate
Comprehensive guide to building LLM-powered autonomous agents and intelligent assistants for real-world business applications by experienced author with 20+ years industry experience
Master production-ready agent frameworks including behavior trees, agentic behavior trees (ABT), collaborative multi-agent systems, and self-improving agents with feedback loops
Advanced topics include memory systems, knowledge management, prompt engineering with Prompt Flow, speech and vision capabilities, and high-stakes negotiation agents
Hands-on implementation with cutting-edge tools: GPT Nexus, Semantic Kernel, CrewAI, vector databases, and real agent orchestration beyond toy examples
Practical guide to designing single and multi-agent systems by seasoned ML engineer with experience at Uber, ServiceNow, and Microsoft with Stanford/Cambridge background
Comprehensive coverage from agent fundamentals to advanced multi-agent coordination including democratic, manager, hierarchical, and actor-critic approaches
Production-focused architecture covering user experience design, orchestration patterns, knowledge management, Graph RAG, and validation frameworks for enterprise deployment
Advanced topics include parametric learning, fine-tuning foundation models, experiential learning, automated skill development, and scaling evaluation sets for real-world systems