Backend Integration Engineer

We are seeking a skilled and enthusiastic Prompt Engineer with substantial experience in Prompt Engineering and LLM-based application development. As a valued member of our team, you will be involved in the construction, deployment, and expansion of GenAI-powered applications utilizing cutting-edge technologies in AI, full-stack development, and cloud computing. Your primary responsibilities will include designing and creating AI applications utilizing Large Language Models (LLMs) like GPT-4, Claude, or LLaMA, crafting, optimizing, and assessing prompts for various use cases, and showcasing proficiency in Python, JavaScript (React/Node.js), or other full-stack technologies. You will be tasked with developing responsive frontend interfaces using frameworks such as React.js, Vue.js, integrating with AI APIs (OpenAI, Azure OpenAI, Cohere, etc.), and working with frameworks like LangChain, LlamaIndex, and RAG pipelines. Furthermore, you will implement vector databases like Pinecone, Weaviate, FAISS for knowledge retrieval, collaborate with cross-functional teams for system integration and deployment, and ensure adherence to best practices in coding, testing, DevOps, and cloud deployment. The ideal candidate should possess proficiency in Python, JavaScript (React/Node.js), or other full-stack technologies, familiarity with AI/ML frameworks (e.g., TensorFlow, PyTorch, Hugging Face), hands-on experience with Prompt Engineering for Large Language Models (LLMs) such as GPT, Claude, LLaMA, and knowledge of RAG (Retrieval-Augmented Generation), LangChain, or LlamaIndex. Additionally, a good understanding of OpenAI, Azure OpenAI, or similar AI APIs, backend development using FastAPI, Flask, or Express, frontend development using React or Vue.js, and experience with CI/CD, Docker, and cloud platforms (AWS, Azure, GCP) is required. Desirable skills include experience with LangChain, LlamaIndex, or similar frameworks, familiarity with tools like Hugging Face, OpenAI Playground, Azure OpenAI Studio, knowledge of MLOps tools and data pipelines, and an understanding of prompt tuning and fine-tuning LLMs.,