Available for AI Projects

AI Agent Developer · GenAI Engineer

I buildintelligent AI systems.

I design and build AI agents, RAG systems, automation workflows, and production-ready LLM applications that solve real problems.

Explore My Work Hire Me Download CV

GitHub ↗LinkedIn ↗

Building AI Systems

AI AGENT

Agent

LangGraph

Knowledge

RAG

Tools

MCP

Automation

n8n Workflows

LLM + Tools + Automation

About Me

Building useful AI,not just demos.

I'm an AI Agent Developer and GenAI Engineer focused on designing intelligent systems that combine LLMs, agents, knowledge, tools, automation, and production-ready backends.

Engineering Mindset

I enjoy taking an idea from problem → architecture → implementation → deployment.

My work focuses on practical AI engineering: connecting models with tools, building agent workflows, retrieving useful knowledge, integrating APIs, and creating systems that can actually be used outside a notebook.

I'm continuously improving my skills in agentic AI, advanced RAG, automation, evaluation, and production deployment.

Explore my work

AI Agents

Building intelligent agents that reason, use tools, maintain context, and execute multi-step tasks.

RAG Systems

Designing knowledge-grounded AI systems that retrieve relevant information before generating responses.

AI Automation

Connecting AI models, APIs, tools, and workflows to automate real-world business processes.

Production AI

Turning AI prototypes into usable applications with APIs, databases, Docker, and cloud deployment.

10+

AI Projects

AI Technologies

AI Agents

24/7

Learning & Building

Selected Work

Systems I'vebuilt.

A selection of AI systems, agents, automation workflows, and machine learning applications built around real engineering problems.

View GitHub

AI System

LangGraph

Planner

Agent

Supervisor

Flight

AviationStack

Weather

OpenWeather

State

PostgreSQL

Deploy

Docker + Render

Featured Project

Multi-agent travel orchestration

01 / Multi-Agent AIMulti-Agent System

AI Travel Planning System

A production-oriented multi-agent travel planner that coordinates specialized AI agents for flights, hotels, weather, budget, and itinerary generation.

Engineering Highlights

Built with LangGraph orchestration, supervisor guardrails, human-in-the-loop approval, external APIs, persistent state, and cloud deployment.

Multi-Agent

Tool Calling

HITL

Persistent State

LangGraphGroqFastAPIPostgreSQLDockerRender

Case Study GitHub

02Project

AI Automation

Personal AI Assistant

An AI-powered personal assistant designed around n8n workflows, webhooks, Gmail, Google Calendar, and intelligent task automation.

Designed to connect everyday productivity tools with AI-driven workflows and automation.

n8nAI AutomationWebhooksGmailGoogle Calendar

GitHub

03Project

Agentic AI

AI Content Agent

A LangGraph-based content generation system that researches topics, creates structured content, uses external tools, and supports human approval.

Combines LLM reasoning, web research, MCP tools, workflow control, and human-in-the-loop interaction.

LangGraphGroqTavilyMCPStreamlit

GitHub

04Project

AI + Backend

JobFlow AI

An AI-powered job matching backend that parses resumes and evaluates job opportunities based on candidate information.

Built a FastAPI backend with PDF resume parsing, job-data integration, ranking logic, and match scoring.

PythonFastAPIAI MatchingPDF ParsingREST API

GitHub

05Project

MCP

MCP AI Agent

An AI agent connected to MCP tools for performing calculations and retrieving information through structured tool calls.

Demonstrates practical tool calling, MCP integration, agent reasoning, and external capability access.

MCPAI AgentTool CallingPythonLLM

GitHub

06Project

Conversational AI

LangGraph College AI Assistant

A conversational college assistant built with LangGraph that maintains context and uses structured agent workflows.

Implemented conversational memory, LangGraph workflows, Groq-powered responses, and a Streamlit interface.

LangGraphGroqMemoryStreamlitPython

GitHub

07Project

Computer Vision

Brain Tumor Detection

A deep learning computer vision application for brain tumor classification using CNN and transfer learning.

Achieved approximately 97% classification accuracy using an EfficientNet-based transfer learning approach.

CNNEfficientNetTransfer LearningPyTorchStreamlit

GitHub

08Project

NLP

T5 Dialogue Summarization

A transformer-based NLP system designed to summarize conversational dialogue using a fine-tuned T5 model.

Implemented model training, evaluation, and deployment using the Hugging Face ecosystem.

T5TransformersNLPPyTorchHugging Face

GitHub

Building something with AI?

Let's turn the idea into a reliable, useful system — from architecture to deployment.

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Technical Skills

The stack behindthe systems.

My focus is building intelligent applications where models, agents, tools, data, APIs, and automation work together.

Primary Focus

Core

AI & LLM

Core AI stack

Generative AILLMsPrompt EngineeringTransformersRAGAI AgentsLangChainLangGraphMCP

Core

Agentic AI

Intelligent systems

Agent ArchitectureMulti-Agent SystemsTool CallingMemorySubgraphsHuman-in-the-LoopLangSmithEvaluation

Core

AI Automation

Workflow systems

n8nAI AutomationWorkflow AutomationGoogle APIsTool IntegrationMCP ToolsWebhooks

Supporting Stack

Machine Learning

ML & deep learning

Machine LearningDeep LearningCNNANNRNNNLPTransfer LearningPyTorchScikit-learn

Backend & APIs

Application layer

PythonFastAPIREST APIsPostgreSQLSQLWebhooksAPI Integration

Deployment & Infrastructure

Production stack

DockerAWSRenderStreamlitGitGitHubEnvironment Variables

How I Engineer

Beyond knowing the tools.

Technologies change quickly. The engineering principles behind reliable AI systems matter even more.

System Thinking

Designing AI applications as complete systems rather than isolated models.

Tool Integration

Connecting LLMs with APIs, databases, tools, workflows, and external services.

Context & Memory

Building systems that retrieve useful information and maintain meaningful context.

Production Mindset

Thinking about evaluation, deployment, reliability, and maintainability.

Always learning. Always building.

Currently exploring advanced RAG, AI automation, evaluation, and production-grade agent systems.

See my work

Engineering Process

From problem toproduction.

I approach AI projects as engineering systems—not just model demos. Every stage has a purpose, from understanding the problem to deploying a reliable product.

Define the problem

Understand

I first understand the business problem, users, constraints, data, and the outcome the system needs to achieve.

Output

Clear problem definition

Plan the architecture

Design

I choose the right architecture, models, agents, data flow, tools, and integrations before writing the core implementation.

Output

System architecture

Develop the AI system

Build

I build the application layer, agent logic, prompts, RAG pipeline, APIs, memory, and supporting backend components.

Output

Working AI system

Give AI real capabilities

Connect

I connect models with APIs, databases, MCP tools, automation workflows, external services, and business systems.

Output

Tool-enabled AI

Test & improve

Evaluate

I test outputs, agent behavior, retrieval quality, failure cases, latency, and overall system reliability.

Output

Reliable AI behavior

Ship to production

Deploy

I package and deploy the application using containers, cloud services, environment configuration, and production infrastructure.

Output

Production deployment

Typical AI System Flow

User

LLM

Agent

Tools

Knowledge

Evaluation

Production

The goal isn't simply to make an AI model respond. The goal is to build a useful, reliable, maintainable system around it.

Start a project

AI Playground

See how the systemsactually work.

Explore simplified versions of the architectures I build: AI agents, retrieval systems, and tool-connected AI workflows.

AI Agent

An agent reasons through a task, chooses the right tools, and executes a workflow.

Interactive Demo

User Request

Agent

Reasoning

Tools

Result

AI Portfolio Assistant

Ask Ashfaque AI

Ask about my skills, projects, technologies, AI engineering experience, or how I can help with your next AI project.

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Portfolio KnowledgeConversation MemoryNext.js APIGroq LLM

Built to demonstrate real AI engineering.

Architecture first. Useful systems second. Animations last.

Work with me

Available for AI projects

Hire Me

Let's build somethingintelligent.

Have an AI idea, automation problem, or product that needs intelligent features? I can help turn it into a working system.

AI Agents

Intelligent agents that reason, use tools, maintain context, and complete multi-step tasks.

LangGraph AgentsMulti-Agent SystemsTool CallingMemory & HITL

RAG & Knowledge Systems

AI systems that connect LLMs with your private documents, databases, and knowledge sources.

Document RAGVector SearchKnowledge AssistantsContext Retrieval

AI Automation

Automated workflows that connect AI with business tools and eliminate repetitive manual work.

n8n AutomationGoogle APIsWebhooksWorkflow Agents

LLM Applications

Production-ready AI applications with modern backends, APIs, interfaces, and deployment.

FastAPI BackendsLLM ApplicationsMCP IntegrationsDocker Deployment

Have a project in mind?

Tell me what you're building.

Whether you need an AI agent, RAG system, automation workflow, or complete LLM application, let's discuss the problem and build the right solution.

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AI Agents · GenAI · RAG · Automation · Production Systems

Contact

Let's turn your idea into an AI system.

Have a project, startup idea, automation problem, or AI product in mind? I'd love to hear about it.

ashfaqueahmed3150@gmail.com

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Available for freelance & remote AI projects

AI Agent Developer · GenAI Engineer