Job Description: AI Engineer

Experience Required: 3+ years (with minimum 1 year in AI/ML/LLM projects)
 Location: Bangalore
 Employment Type: Full-time

About the Role

We are looking for an AI Engineer with a solid foundation in software development (Python) and some hands-on experience in AI/ML projects, especially Large Language Models (LLMs). You will work with our team to build, fine-tune, and deploy AI-powered applications that solve real-world business problems.

• Hands-on experience with at least one Agentic AI framework: Langchain, LangGraph, or CrewAI.
• Strong understanding of agent-based architectures and autonomous workflows.
• Experience in Natural Language Processing (NLP) and working with LLMs (OpenAI, Anthropic, etc.).
• Proficient in Prompt Engineering for various use cases (conversational agents, tools, retrieval, etc.).
• Working knowledge of Retrieval-Augmented Generation (RAG) techniques and vector stores (e.g., FAISS, Pinecone).
• Ability to integrate external APIs, work with databases, and manage dynamic knowledge sources.
• Experience with tools and platforms like OpenAI, LangSmith, Weaviate, or ChromaDB is a plus.
• Familiarity with text-to-image models such as DALL·E 3, Midjourney.
• Solid understanding of machine learning principles, including feedback loops and performance tuning.

• Build, deploy, and maintain AI agents using frameworks such as Langchain, LangGraph, or CrewAI.
• Develop and optimize prompts for LLMs using prompt engineering best practices.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines to enhance the contextual capabilities of agents.
• Collaborate with APIs, databases, and external tools to enable agents to perform complex tasks autonomously.
• Engage in real-time user interactions, enabling agents to provide intelligent, context-aware responses.
• Implement feedback and reinforcement learning mechanisms to iteratively improve agent performance.
• Retrieve and process data from multiple internal and external sources for analysis and decision-making.
• Utilize NLP techniques to understand and generate human-like language.
• Create and fine-tune text-to-image prompts using models like DALL·E 3 to support multimodal tasks.