Ai Full Stack Engineer

Job Summary

We are looking for a highly motivated AI Full Stack Developer to join our team to build, deploy, and maintain end-to-end applications that leverage generative AI models and agentic architectures. As a Full-stack AI Developer, will bridge the gap between AI research and production-ready applications, working across the entire stack from frontend interfaces to backend logic and machine learning models. Will be responsible for building, testing, and scaling AI-driven products.

Key Responsibilities

• AI Application Development: Develop and maintain end-to-end AI applications, from user interfaces to backend logic, focusing on AI-powered features.
• ML Model Integration: Implement machine learning models (using frameworks like PyTorch, TensorFlow, or scikit-learn) into web and mobile applications.
• Backend & API Engineering: Build and maintain scalable backend services and RESTful APIs, often integrated with large language models (LLMs) and agentic frameworks.
• Frontend Development: Create interactive, responsive front-end components for user interfaces using modern frameworks like React, Vue, or Next.js.
• MLOps & Deployment: Manage end-to-end life cycles for production, including deployment workflows using Kubernetes, Cloud Run, or containerization tools to ensure high-performance applications.
• Database Management: Manage both relational and NoSQL databases to support AI-powered functionality.
• Collaboration: Work closely with data scientists, product managers, and designers to turn AI capabilities into user-focused products.

Required Qualifications & Skills

• Experience: Proven experience (8 -15 yrs) as a Full Stack Developer with specialized experience in AI model deployment.
• Backend Skills: Strong proficiency in Python and frameworks like FastAPI or Django.
• Frontend Skills: Experience with modern JavaScript frameworks (React.js + Node.js,Next.js, Plotly Dash + FastAPI).
• AI/ML Knowledge: Familiarity with AI model integration (e.g., OpenAI API, LangChain, PyTorch).
• Cloud/DevOps: Experience in Cloud platforms (AWS, GCP, Azure) and container technologies (Docker, Kubernetes).
• Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field.

Preferred Qualifications

• Experience with generative AI and agentic architectures.
• Understanding of data privacy and security in AI applications.
• PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field