Innovative AI Researcher

7 days ago

Bengaluru, Karnataka, India beBeeResearch Full time ₹ 1,50,00,000 - ₹ 2,50,00,000
AI Researcher - Innovate with Large Language Models

Ambitious AI/ML researcher sought to drive innovation in cutting-edge technologies.

  • Develop and implement RAG-based knowledge systems using vector databases like Azure AI Search or similar technologies.
  • Fine-tune LLMs for domain-specific applications using techniques like LoRA / QLoRA.
  • Design and develop real-time RAG systems for dynamic, context-aware decision making.
  • Utilize graph-based RAG techniques and Graph Neural Networks (GNNs) for enhanced contextual reasoning.
  • Integrate multimodal transformers combining text, image, and audio data.
  • Lead performance optimization efforts using Redis caching, semantic indexing, and latency reduction techniques.

Key Skills:

  • Expertise in LLMs, vision transformers (ViTs), diffusion models, RL, or multi-agent systems.
  • RAG & Vector Databases: Expertise in building and querying knowledge bases using Retrieval-Augmented Generation with Azure AI Search or similar technologies.
  • LLM Fine-Tuning: Hands-on experience with efficient finetuning techniques (LoRA/QLoRA) for specializing models on custom datasets.
  • Proficiency with libraries for data transformation and comparison, such as JSON Patch and DeepDiff.
  • Quantum Computing: Understanding of quantum algorithms and tools like IBM's Qiskit and Google's Cirq.
  • Multimodal transformers - integrating text, image, and audio data to create models.
  • Graph-based RAGs for contextual reasoning and incorporating knowledge connections from graph neural networks. Real time RAG systems to handle dynamic and up-to-date information.

Requirements:

  • LLM & RAG Architecture Expertise: (Must have)
  • Hands-on experience with Retrieval-Augmented Generation (RAG) architectures using embeddings via Azure AI Search and Databricks.
  • Proficient in implementing semantic search capabilities.
  • Familiar with MCP servers for scalable deployment.
  • Agentic AI & Orchestration: (Must have)
  • Experience with autonomous decision support using LangGraph.
  • Skilled in agent orchestration using Microsoft Copilot Studio and CrewAI.
  • Performance Optimization: (Must have)
  • Working knowledge of latency reduction techniques for RAG-based applications, including Redis-based caching.
  • LLM Fine-Tuning: (Must have)
  • Practical understanding of fine-tuning methods such as LoRA (Low-Rank Adaptation).
  • Model Selection & Prompting: (Must have)
  • Awareness of the latest LLMs tailored to specific use cases (e.g., Claude, Gemini, GPT series).
  • Understanding of prompt engineering requirements across different models.
  • Cost Estimation: (Must have)
  • Ability to calculate and optimize costs for API-based model usage.
  • Functional/Team experience (Must have)
  • Expertise with diverse AI uses cases - Must
  • Business and Domain Understanding : Must
  • Track record of research (papers, patents, open source)
  • Client management

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