Manas Jha

AI Researcher

Delhi, India1 yr 4 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Expert in AI and machine learning solutions.
Proven experience in computer vision projects.
Strong foundation in full-stack development and cloud architecture.

Stackforce AI infers this person is a Machine Learning and Computer Vision Engineer with a focus on AI-driven solutions.

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Skills

Core Skills

Machine LearningArtificial Intelligence (ai)Computer VisionSoftware Development

Other Skills

C (Programming Language)Camera Stream IntegrationCoding ExperienceCustom CNNData AnalysisEngineeringJavaMLOpsMachine Learning AlgorithmsMicrosoft OfficeOpen-Source SoftwareOpenCVPythonPython (Programming Language)Real-time Detection

About

I'm an AI developer and B.Tech IT student at Gautam Buddha University, focused on building real-world solutions with machine learning, computer vision, and large language models (LLMs).I’ve worked on AI-powered chatbots using Retrieval-Augmented Generation (RAG), emotional intelligence systems for mental health, and stubble burning detection prototypes using satellite data. I also build computer vision software for classification and object detection, and have hands-on experience with LLM fine-tuning for domain-specific tasks.With a strong base in full-stack development, vector databases, and cloud architecture (GCP certified), I’m passionate about creating human-centric, intelligent AI systems.

Experience

Marshee pet tech

Machine Learning Engineer

May 2025 – Present · 10 mos · Delhi, India · On-site

Development of AI product
Making dog breed detection system
Dogs age detection system

Python (Programming Language)Artificial Intelligence (AI)Machine LearningMachine Learning AlgorithmsSupervised LearningMLOps

Agro farm ventures private limited

Computer Vision Engineer

Apr 2025 – May 2025 · 1 mo · Noida, Uttar Pradesh, India · Hybrid

I developed an end-to-end inventory management and box detection system using a live camera feed, combining both traditional CNNs and YOLOv11 for optimized performance.
🔍 Key Features:
Designed a custom Convolutional Neural Network (CNN) to classify and identify unique inventory items.
Integrated YOLOv11 for real-time object detection, ensuring high accuracy and fast response in live environments.
Enabled seamless box detection and tracking via camera, reducing manual inventory errors.
Built a lightweight and scalable Python-based backend with OpenCV, NumPy, and TensorFlow/PyTorch.
💡 Impact:
This solution not only increases warehouse efficiency but also reduces human effort and improves stock accuracy through automation.