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AI Engineer with less than a year in LLM Applications & Computer Vision
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AI Engineer specializing in LLM applications, computer vision, and ML infrastructure. Experienced in designing, deploying, and scaling end-to-end AI systems, with a focus on building reliable, production-ready solutions that deliver measurable real-world impact.
National University of Sciences & Technology (NUST)
BS · Computer Science
August 1, 2022 – June 30, 2026
Neuronix Technologies
AI Engineer Intern
March 1, 2026 – April 1, 2026
India
Data BI LLC
Data & AI Associate Engineer
June 1, 2025 – December 1, 2025
India
Pak Econ Bot
June 24, 2026 – Present
Production agentic RAG system with a hand-rolled ReAct loop over LLaMA 3.3 70B; FastAPI backend on AWS EC2 and React frontend on AWS S3, deployed with automated CI/CD via GitHub Actions. Indexed ~1,200 chunks in Pinecone (384-dim cosine) with metadata filtering by section and content type; agent exposes 4 tools – semantic search, section lookup, table/numeric queries, and safe arithmetic evaluation. Switched production inference to ONNX Runtime instead of PyTorch, shrinking the Docker image by ~700 MB; per-session conversation memory with a 5-turn sliding window and isolated agent state per user.
View ProjectVoice 2 Action
June 24, 2026 – Present
End-to-end agentic system that converts informal Roman-Urdu voice notes into structured tasks using Qwen2.5-3B (QLORA fine-tuned) with Faster-Whisper STT and local inference via Ollama, fully containerized with Docker Compose. Built a LangGraph-based reasoning agent that detects genuine ambiguity, asks a single targeted follow-up, and performs grounded state merging while preventing hallucinated fields (deadlines, people, or tasks). Integrated optional Notion execution API for real task creation, with deterministic agent traces and safe execution gating; system achieves >80% exact match on held-out evaluation after iterative dataset refinement.
View ProjectProject Inspector
June 24, 2026 – Present
AI-powered codebase analysis tool on Azure Container Apps with GitHub Actions CI/CD; parses Python AST across 15+ files in under 60 seconds. Renders dependency graphs via NetworkX and Graphviz; base64-embedded SVGs keep the API stateless and scalable. Dual-LLM fallback (Gemini 2.5 Flash → LLaMA 3.1 8B via Groq) with response cache cutting redundant calls by ~60%.
View ProjectCultural Fit Analysis
The candidate's diverse personal projects (Pak Econ Bot, Voice 2 Action, Project Inspector) showcase initiative, self-driven learning, and a passion for AI beyond academic or professional requirements. The experience in both LLM and computer vision domains, coupled with MLOps skills, indicates a broad technical curiosity and adaptability. The focus on production-readiness and measurable impact aligns with a results-oriented culture. The candidate's early career stage combined with advanced project work suggests a high potential for growth and a proactive attitude.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a strong problem-solving aptitude, an ability to work with complex systems, and a focus on delivering measurable real-world impact. The detailed descriptions of architectural choices, optimizations, and evaluation metrics suggest a methodical and results-oriented approach. The use of CI/CD and containerization indicates an understanding of modern development and deployment practices, which aligns well with operational efficiency.