We are seeking a skilled and innovative AI Engineer with Full Stack Development expertise who has experience working with Generative AI (Gen AI) models, such as Large Language Models (LLMs), and implementing these solutions into web applications. This role combines the responsibilities of a full-stack developer with advanced knowledge of machine learning models, APIs, and cloud infrastructure, focusing on integrating state-of-the-art AI capabilities into user-friendly and efficient web-based applications. Key Responsibilities: Integrate LLMs and other Gen AI models into web applications through efficient API design and implementation. Build and optimize API endpoints to allow seamless, real-time interaction between front-end applications and back-end AI models. Design and develop secure, scalable, and high-performing microservices for AI model deployment. Front-End Development: Design and develop responsive and intuitive front-end applications using React.js to enhance the user experience of AI-powered applications. Implement front-end components to visualize, interpret, and manage AI model outputs effectively. Back-End Development & AI Pipelines: Develop robust back-end systems in Python to support the deployment, scalability, and maintenance of Gen AI models. Build and maintain data pipelines, including preprocessing data and post-processing AI model outputs for consumption by applications. Implement best practices for handling sensitive data and maintaining model performance. Infrastructure & Deployment: Use Kubernetes and Docker for containerization and orchestration to ensure scalable deployment and management of AI applications. Implement continuous integration and continuous deployment (CI/CD) pipelines for automated testing and deployment of code changes. Maintain a scalable and secure cloud infrastructure, leveraging platforms such as Google Cloud Platform or Azure for model training, storage, and deployment. LLM and Gen AI Ecosystem Expertise: Utilize vector databases (e.g., Pinecone, Weaviate, or Faiss) to manage and retrieve embeddings for efficient similarity search and recommendation systems. Work with frameworks for model development and deployment, including Hugging Face Transformers, Lang Chain, and Open AI. Optimize and fine-tune LLMs to improve performance based on application needs.
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