We are seeking a highly skilled and motivated Data Scientist(LLM, RAG) to join our team and contribute to the development of cutting-edge AI solutions. The ideal candidate will have hands-on experience in building RAG knowledge systems and expertise in frameworks such as LangChain, LangGraph, or LlamaIndex. This role will focus on designing, implementing, and optimizing LLM-based applications to solve complex problems, leveraging pre-trained models and integrating them into scalable workflows. Additionally, the candidate will work on Agentic AI systems that enable autonomous decision-making and MCP (Memory, Context, and Personalization) capabilities to create intelligent, context-aware applications. If you are passionate about pushing the boundaries of AI and enjoy working in a fast-paced, innovative environment, wed love to hear from you.
[Core Responsibilities]
- Design and Develop Agentic AI Systems: Build and optimize Survey Agentic AI platforms that leverage memory, context, and personalization (MCP) to create autonomous and intelligent agents.
- Implement RAG Pipelines: Develop and deploy Retrieval-Augmented Generation (RAG) systems to enhance knowledge retrieval and generation capabilities.
- Leverage AI Toolkits: Work with platforms like Cohere, Microsoft AI tools (e.g., Azure OpenAI, Cognitive Services), and other state-of-the-art AI technologies to deliver innovative solutions.
- Stay Updated: Keep up with the latest advancements in AI/ML technologies and contribute to the teams knowledge base.
[Required Skills]
- RAG Knowledge Systems: Proven experience in building and deploying Retrieval-Augmented Generation (RAG) pipelines.
- Framework Expertise: Hands-on experience with tools and frameworks such as LangChain, LangGraph, or LlamaIndex.
- AI Toolkits: Experience with Cohere, Microsoft AI tools (e.g., Azure OpenAI, Cognitive Services), and other state-of-the-art AI platforms.
[Bonus/Preferred Experience]
- Agentic AI: Familiarity with agentic AI concepts and frameworks for building autonomous AI agents.
- MCP: Knowledge of designing AI systems with memory, context-awareness, and personalization capabilities.
- Cloud and Deployment: Experience with deploying AI models on cloud platforms (e.g., AWS, GCP, Azure) and optimizing for production environments.
[Candidate profile]
- Experience: 3+ years of experience in GenAI engineering, with a focus on LLM-related applications.
- Passion for AI: A deep interest in advancing the state of AI and applying it to real-world challenges.
- Self-Starter: Ability to work independently, take ownership of projects, and deliver results in a dynamic environment.
- Team Player: Strong interpersonal skills and a collaborative mindset to work effectively with diverse teams.
