a
aimanmalik0202

Aiman M

@aimanmalik0202

AI Engineer

Pakistan
Anglais, Ourdou
Certaines informations sont présentées en anglais.
À propos de moi
AI/ML Engineer specializing in Computer Vision & GenAI. I build production-grade systems: CCTV compliance pipelines for intoxication/violence detection (YOLO, pose estimation, MLP classifiers), RAG chatbots (LangChain, Pinecone, Groq), and custom ML models end-to-end. Comfortable across the full stack — FastAPI/Flask, HuggingFace, OpenAI/Gemini/Llama APIs, AWS Bedrock, Vertex AI, GitHub Actions CI/CD. I turn messy real-world data and footage into reliable, deployed AI — not just notebooks. Let's build something that actually works in production.... Plus d’infos

Compétences

a
aimanmalik0202
Aiman M
hors ligne • 

Voir mes services

Développement de chatbots d'IA
I will build rag chatbots and genai applications using langchain

Expérience professionnelle

IT_Force

IT Force

Temps plein • 8 mos

Associate Machine Learning Engineer

Apr 2026 - Present3 mos

Associate ML Engineer | Nexpred Solutions | 2026 Building an end-to-end AI Compliance Pipeline for CCTV surveillance, specializing in intoxication detection — my most technically involved ML project to date. - Labeled and annotated pose-based datasets from scratch for behavioral analysis - Researched three behavioral cues (staggered walk, head drop, slumped posture) using YOLOv11s pose estimation - Extracted skeletal keypoints and applied threshold-based gating to isolate high-signal training samples - Trained individual MLP classifiers per cue, then built a probability-based fusion ensemble — boosting accuracy and robustness beyond any single-cue model - Developing an age classification module for the same compliance system - Architecting the system for production deployment, not just as a research prototype

AI Intern

Jan 2026 - Jun 20265 mos

AI Intern | Corvit Networks | 2026 Worked across the full GenAI pipeline — from vectorization and vector databases to fine-tuning and RAG architectures — with hands-on exposure to LLMOps on Google Vertex AI and AWS Bedrock. - Built two chatbots: a real-time Telegram bot powered by GPT-3.5-Turbo, and a RAG-based Medical Chatbot using LangChain, Pinecone, and Groq API for low-latency, grounded responses - Worked hands-on with HuggingFace and OpenAI APIs across the GenAI development lifecycle - Applied CI/CD practices using GitHub Actions to automate build, test, and deployment pipelines for AI applications - Gained practical exposure to LLMOps across two major cloud platforms (Vertex AI, Bedrock) — bridging the gap between experimental LLM work and production deployment

Trainee AI Engineer

CureMD • Temps plein

Jul 2025 - Oct 20253 mos

Trainee AI/ML Engineer | CureMD | 2025 Developed a full-stack machine learning application, building an OOP-based backend integrated with FastAPI to let users train Linear Regression models via Gradient Descent with configurable hyperparameters — with an HTML/CSS/JS frontend for real-time interaction. - Designed and implemented the training pipeline from scratch, exposing hyperparameters (learning rate, iterations, etc.) as user-configurable inputs - Built a BMI prediction model using real clinical data from MongoDB, handling data preprocessing, exploratory data analysis, and feature engineering - Tuned and compared multiple ML models to optimize R² performance for the BMI prediction task - Bridged full-stack development and applied ML — not just model training, but making it usable through a working web interface