AI Engineer

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.