AIML Engineer

As an AI Engineer, your role involves designing and implementing intelligent systems using agentic AI capable of autonomous decision-making and task execution. You will integrate advanced machine learning models with reasoning, planning, and interaction capabilities to build scalable, intelligent applications. Your expertise in LLMs, prompt engineering, and web development will be crucial for deploying AI-powered web applications successfully. Key Responsibilities: - Design and develop agentic AI systems with autonomous task execution capabilities. - Integrate Large Language Models (LLMs) with reasoning, planning, and interaction frameworks. - Implement Retrieval-Augmented Generation (RAG) architectures using vector databases. - Engineer effective prompts and workflows for LLM-based agents. - Utilize agentic libraries (e.g., LangChain, AutoGPT, CrewAI, etc.) to build modular and scalable AI agents. - Develop and deploy web applications that interface with AI systems. - Collaborate with cross-functional teams to define system requirements and deliver robust solutions. - Optimize performance, scalability, and reliability of AI-powered applications. Must-Have Skills: - Programming: Proficiency in Python and relevant AI/ML libraries. - LLMs: Experience working with models like GPT, Claude, Mistral, or open-source equivalents. - Prompt Engineering: Ability to design and refine prompts for complex tasks. - Vector Databases: Hands-on experience with FAISS, Pinecone, Weaviate, or Chroma. - RAG Architecture: Deep understanding of retrieval-based generation pipelines. - Agentic Libraries: Familiarity with frameworks like LangChain, AutoGPT, CrewAI, or similar. - Web Development: Prior experience in frontend/backend development and web app deployment (e.g., using Flask, FastAPI, React, Docker). - Deployment: Experience deploying applications on cloud platforms (AWS, Azure, GCP) or containerized environments. As an AI Engineer, your role involves designing and implementing intelligent systems using agentic AI capable of autonomous decision-making and task execution. You will integrate advanced machine learning models with reasoning, planning, and interaction capabilities to build scalable, intelligent applications. Your expertise in LLMs, prompt engineering, and web development will be crucial for deploying AI-powered web applications successfully. Key Responsibilities: - Design and develop agentic AI systems with autonomous task execution capabilities. - Integrate Large Language Models (LLMs) with reasoning, planning, and interaction frameworks. - Implement Retrieval-Augmented Generation (RAG) architectures using vector databases. - Engineer effective prompts and workflows for LLM-based agents. - Utilize agentic libraries (e.g., LangChain, AutoGPT, CrewAI, etc.) to build modular and scalable AI agents. - Develop and deploy web applications that interface with AI systems. - Collaborate with cross-functional teams to define system requirements and deliver robust solutions. - Optimize performance, scalability, and reliability of AI-powered applications. Must-Have Skills: - Programming: Proficiency in Python and relevant AI/ML libraries. - LLMs: Experience working with models like GPT, Claude, Mistral, or open-source equivalents. - Prompt Engineering: Ability to design and refine prompts for complex tasks. - Vector Databases: Hands-on experience with FAISS, Pinecone, Weaviate, or Chroma. - RAG Architecture: Deep understanding of retrieval-based generation pipelines. - Agentic Libraries: Familiarity with frameworks like LangChain, AutoGPT, CrewAI, or similar. - Web Development: Prior experience in frontend/backend development and web app deployment (e.g., using Flask, FastAPI, React, Docker). - Deployment: Experience deploying applications on cloud platforms (AWS, Azure, GCP) or containerized environments.