🚀 **Frontend engineer | typescript developer | frontend developer | frontend react developer | frontend typeScript developer | react developer **
🎯 Specializing in React.js, TypeScript, modern frontend technologies — and building AI-enabled frontends

I'm a passionate software engineer from Pakistan 🇵🇰 with 4 years of experience crafting exceptional user experiences and building scalable frontend solutions. My expertise lies in translating complex Figma designs into pixel-perfect, high-performing interfaces, optimizing API integrations, and leading teams toward successful project outcomes.

Lately, I'm expanding into applied AI — building LLM-integrations, Retrieval-Augmented Generation (RAG) systems, agentic AI experiments, and practical prompt engineering for production-grade apps.

When I'm not coding, you'll find me exploring new technologies, solving real-world problems, mentoring junior developers, or contributing to open-source projects.

• Frameworks / Libraries: React.js, Next.js, Vue.js, Nuxt.js, Node.js, Redux, Redux Toolkit, React Query
• Languages: JavaScript, TypeScript, HTML, CSS
• Tooling: TailwindCSS, MUI, Bootstrap, Jest, CI/CD, Styled Components
• AI / LLM Stack: OpenAI, Cohere, Hugging Face, LangChain, LlamaIndex, Pinecone, Weaviate, FAISS, embeddings, vector databases, prompt engineering, RAG (Retrieval-Augmented Generation), LLM integration patterns, agentic AI prototypes
• Leadership: Project Leadership, Agile/Scrum, Mentoring, Code Reviews, Cross-functional Collaboration
• Other Skills: Teamwork, Communication, Time Management, Problem-Solving

Currently, I’m a frontend engineer at eBrainX Solutions, creating event management solutions that drive real impact. Previously, I worked at Techling Pvt, focusing on scalable data pipeline solutions, collaborating with global teams, and integrating complex APIs into beautiful, user-friendly interfaces.

• Daspire Website
  Redesigned and optimized the platform for enhanced user engagement and performance.

• Cohere
  Architected the frontend for a SaaS platform focused on seamless virtual collaboration.

• Cazvid
  Contributed to the world’s largest video resume-based job board, focusing on user journeys and UI performance.

• Agentic AI Playground — Personal experiments with multi-step agents (planner + executor + tool invocations). Repo: https://github.com/Usama-dev182/agentic-ai-playground (placeholder)
• Prompt Engineering Playbook — Curated prompt templates and few-shot examples for intent steering, system messages, and safety prompts. Repo: https://github.com/Usama-dev182/prompt-playbook (placeholder)
• RAG Documentation Search — A demo that ingests docs, creates embeddings, stores them in a vector DB, and serves a conversational UI. Repo: https://github.com/Usama-dev182/rag-docs-search (placeholder)
• LLM Integrations & Frontend Patterns — Example apps demonstrating streaming completions, conversation state, and cost-optimized hybrid retrieval+generation.

(Links above are examples — I plan to create/pin these repositories so you can open them directly.)

1. Define the user goal and acceptance criteria (UX + safety).
2. Collect & sanitize source data (documents, logs, APIs).
3. Preprocess and split documents, compute embeddings.
4. Store vectors in a vector DB (Pinecone / Weaviate / FAISS).
5. Build retrieval layer and design prompt templates (RAG).
6. Wire LLMs (OpenAI / Cohere / HF) with chain logic (LangChain/LlamaIndex).
7. Add agentic components (planner, tool invoker, fallback rules) for multi-step tasks.
8. Test with adversarial prompts, measure hallucination rates, tune retrieval.
9. Monitor, log, and optimize cost/latency and add safety controls.
10. Iterate with user feedback and metrics.

• System + User pattern:
  • System: "You are a concise assistant that cites sources and refuses unsafe requests."
  • User: "Summarize the updates in this document and list the top 3 action items."
• Chain-of-thought (structured):
  • "Step 1: Identify key facts. Step 2: Rank them by priority. Step 3: Output a short summary and actions."
• Few-shot for format:
  • Provide 2–3 examples of input → desired output to steer style and verbosity.

(Keep prompts short in production and rely on RAG to ground answers with citations.)

• Ingestion: chunk docs, compute embeddings with an embeddings model.
• Vector store: Pinecone / Weaviate / FAISS (local) for nearest-neighbor retrieval.
• Retriever: top-k results with filtering (metadata, time decay) for relevance.
• Prompt assembly: include retrieved docs, system instruction, and user query.
• LLM call: use temperature/cost tuning, streaming where beneficial.
• Post-processing: extract citations, sanitize outputs, apply safety filters.

• Advanced TypeScript Patterns 🌀
• Backend Development with Node.js 🌐
• Leadership & Mentoring in Tech Teams 🧑‍💼
• Agentic AI, Prompt Engineering, RAG & LLM Integrations 🤖

• GitHub Gists: Usama's Code Snippets
• AI Demos & Notebooks: (coming soon — planning to publish examples and walkthroughs)
• Medium / Blog: Coming Soon!

• I thrive on creating solutions that make users’ lives easier.
• Passionate about leading teams and mentoring aspiring developers.
• Outside of coding, I enjoy family time, exploring new tools, and playing sports.

"Great teams build great software. I believe in leading by example, empowering others, and delivering excellence—one pixel at a time."