I'm Nadeem, a Computer Science student at NUST (SEECS), Pakistan (BS CS, 2024 – 2028) focused on Agentic AI engineering.
I build LLM-powered agents that don't just generate text. They decide, call tools, hit real APIs, and complete multi-step workflows, wrapped in proper backends and usable interfaces.
class Nadeem:
role = "Agentic AI Engineer / Full-Stack AI Developer"
education = "BS Computer Science @ NUST SEECS (2024 – 2028)"
focus = ["AI Agents", "MCP Servers", "Workflow Automation", "AI-Powered Web Apps"]
stack = ["Python", "FastAPI", "LangGraph", "LangChain", "Next.js", "MySQL"]
learning = ["Agent Evaluation", "Tracing & Observability", "Production Reliability"]
open_to = ["Internships", "AI Roles", "Freelance Automation Projects"]|
Python, LLMs, LangGraph, LangChain, tool calling, stateful agent workflows, RAG, and prompt engineering. |
MCP / FastMCP, REST APIs, OAuth 2.0, JWT auth, databases, async processing, and third-party integrations. |
FastAPI, Next.js, React, MySQL, Docker, and deployment of AI-powered applications. |
| Domain | What I Work With | Level |
|---|---|---|
| Agentic AI & LLM Apps | LangGraph, LangChain, tool/function calling, agent workflows, prompt engineering, agent memory & context | ⭐⭐⭐⭐ |
| MCP & AI Integrations | MCP servers & clients, FastMCP, remote MCP, Streamable HTTP, tool design, external API integration | ⭐⭐⭐ |
| Python Backend | FastAPI, REST APIs, SQLAlchemy, async/await, background & scheduled jobs | ⭐⭐⭐ |
| Full-Stack Development | Next.js (App Router), React, Node.js, Tailwind CSS, route handlers | ⭐⭐⭐ |
| Databases | MySQL, SQL, SQLAlchemy, Pinecone, Chroma, vector search | ⭐⭐⭐ |
| Auth & Security Basics | OAuth 2.0, Google OAuth, JWT, Clerk, bearer tokens, credential handling | ⭐⭐⭐⭐ |
| RAG & Embeddings | Semantic retrieval, vector stores, grounding LLM responses | ⭐⭐⭐⭐ |
| Deployment & Ops | Docker fundamentals, environment config, hosted deployments | ⭐⭐ |
| Agent Evaluation & Observability | LangSmith tracing, trajectory & tool evaluation (actively building) | ⭐⭐⭐ |
⭐⭐ Foundational · ⭐⭐⭐ Working knowledge · ⭐⭐⭐⭐ Strong practical knowledge. A self-assessment, not a formal exam.
- 🔗 AI + Backend Integration: connecting LLMs and agents to APIs, databases, and external services
- 🤖 Tool-Enabled AI: giving models structured capabilities instead of relying on generated text alone
- 🔁 Workflow Automation: turning multi-step manual processes into software-driven pipelines
- 🧱 End-to-End Development: frontend, backend, database, and AI layer in a single application
- 🐞 Practical Debugging: auth flows, deployment environments, API communication, and database integration
- 🔀 Cross-Stack Flexibility: Python-based AI backends paired with JavaScript web interfaces
- 📈 Fast, Continuous Learner: from web fundamentals to agent orchestration and MCP
- 🔬 Agent evaluation: measuring output quality, tool-selection accuracy, and trajectories
- 🔭 Tracing & observability with LangSmith
- 🛡️ Reliability engineering: retries, timeouts, malformed outputs, partial failures
- 💸 Cost & token optimization for efficient agent workflows
- 🌐 Remote MCP: more robust, remotely hosted MCP integrations
- 🏗️ Production architecture: testing, maintainability, and monitoring
- 🧩 NestJS and deeper async / concurrency patterns
Open to internships, AI engineering roles, and freelance automation projects.