Subtain Malik
Senior Machine Learning Engineer
Building scalable AI systems that drive measurable business impact
Building scalable AI systems that drive measurable business impact
Senior Machine Learning Engineer with 5+ years of experience building production AI systems for enterprise clients such as USPS and Airbus. Expertise in LLM and Computer Vision pipelines, processing 100K+ daily requests with 23× faster inference. Specializes in scalable, high-performance ML solutions that drive measurable business impact.
Python, C/C++, Javascript, GO, SQL, NoSQL
Machine Learning, DeepLearning, PyTorch, Tensorflow, Computer Vision, LLM, HuggingFace, LangChain, LangGraph, TensorRT, Deepstream, Triton Server, Milvus, Numpy, Pandas, Scikit-learn, FastAPI, Kafka, Elasticsearch, Redis, Docker, AWS, Azure
Pioneered military-grade satellite imagery analysis systems with small object detection achieving 95% accuracy. Built AI diagnostic tools for dermatology and enhanced geospatial monitoring capabilities by 35%.
Engineered enterprise analytics chatbots with LangChain and GPT-4, achieving 40% faster data retrieval. Built centralized AI model platform with automated CI/CD pipelines and optimized video analytics processing with custom NVIDIA DeepStream plugins.
Leading AI development with Agentic AI applications and fine-tuned LLMs achieving 23x faster inference. Architected high-performance AI pipelines processing 100K+ daily requests and built computer vision solutions analyzing 2.3M+ images monthly.
Multi Agent System
A multi-agent system using LangGraph, Ollama, Vector Database, face and voice recognition to extract user-relevant information and respond to queries accordingly.
AI-based Social Media Analysis
Developed complete architecture for AI-based social media analysis including LLM, Transcription, Image Captioning, and Multi-lingual Media Analysis.
Video Analytics Optimization
Implemented Gstreamer plugins and deployed segmentation + pose-estimation YOLO models on Deepstream to optimize FPS of computer vision systems.
Surveillance Monitoring System
Surveillance-based system with ROI-based tracking and monitoring of sensitive areas and activity recognition.
AI-Assisted Data Labeling
Engineered feature matching, interactive segmentation, SAM and YOLO-based auto-annotation tool for efficient data labeling.
Aerial Imagery Research & Analysis Framework
Detect small objects in satellite imagery using YOLO/YOLT architecture, achieving 95% detection accuracy for objects under 10 pixels and reducing analysis time by 80%.
National University of Science and Technology (NUST)
CGPA: 4.0/4.0
Graduation: Dec 2021
Publication: Reimagining Application User Interface (UI) Design using Deep Learning Methods: Challenges and Opportunities (2023)
Open Source: TensorRTX, PytorchX, YOLOv7-Pose