
ai/ml
3 days ago
Role & responsibilities
- Design, develop, and deploy agentic AI systems using cutting-edge frameworks and libraries.
- Integrate Large Language Models (LLMs) with reasoning and planning modules to enable autonomous task execution.
- Implement Retrieval-Augmented Generation (RAG) architectures for dynamic knowledge integration.
- Engineer effective prompts and workflows to optimize agent performance.
- Work with vector databases to store and retrieve semantic information efficiently.
- Apply computer vision techniques for perception and understanding in multimodal environments.
- Develop robust document processing pipelines for intelligent data extraction and analysis.
- Collaborate with cross-functional teams to align AI solutions with business goals.
- Continuously evaluate and improve system performance, scalability, and reliability.
Preferred candidate profile
- Programming: Proficiency in Python and relevant AI/ML libraries.
- LLMs: Experience with fine-tuning, deploying, and integrating large language models.
- Prompt Engineering: Strong understanding of prompt design and optimization strategies.
- Vector Databases: Hands-on experience with tools like FAISS, Pinecone, or Weaviate.
- RAG Architecture: Practical knowledge of building and deploying RAG-based systems.
- Agentic Libraries: Familiarity with LangChain, LangGraph, Autogen, or similar frameworks.
- Computer Vision: Experience with libraries like OpenCV, YOLO, or Detectron2.
- Document Processing: Expertise in OCR, NLP, and structured data extraction from unstructured sources.
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