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, we'd 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 team's 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.