AI - Python Full Stack Developer (Fresher)

Role Overview

• As an AI – Python Full Stack Developer at Thor Signia, you will play a pivotal role in designing, building, and deploying complete AI products from the ground up
• You'll integrate data pipelines, machine learning models, and user interfaces into cohesive, production-ready systems
• This opportunity is ideal for individuals passionate about AI, automation, and building real-world solutions in a collaborative, product-driven environment

Key Responsibilities:

• Develop and integrate AI/ML models using Python frameworks such as Scikit-learn, TensorFlow, and PyTorch
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  Design and implement backend systems leveraging Django or Flask
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  Connect AI models to intuitive user interfaces using React, HTML, CSS, and JavaScript
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  Automate workflows by creating scripts and system-level solutions
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  Preprocess, clean, and manage datasets to support robust AI pipelines
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  Build and expose APIs for deploying models in real-world applications
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  Collaborate on system architecture and design, contributing to strategic technical decisions
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  Debug, optimize performance, and maintain code with version control
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  Participate in code reviews, document solutions, and support timely delivery cycles
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Requirements:

• Proficient in Python with solid object-oriented programming fundamentals
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  Strong grasp of machine learning principles and model training
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  Experience with Scikit-learn, TensorFlow, or PyTorch
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  Solid understanding of Django or Flask for backend development
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  Familiarity with automation scripting and system-level Python workflows
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  Basic frontend skills: HTML, CSS, JavaScript; knowledge of React is a plus
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  Working knowledge of Git, Postman, and API testing practices
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  Excellent problem-solving and debugging abilities
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  Strong communication skills; able to thrive independently
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  Eagerness to learn, adapt, and respond well to feedback
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  Preferred Skills: Exposure to LangChain, Hugging Face, or transformer-based AI tools
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  Understanding of vector databases (eg, ChromaDB, Pinecone)
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  Experience with end-to-end ML pipelines and Docker for deployment