About Job/Role

We are looking for skilled GenAI Engineers with hands-on experience in developing and deploying Generative AI (GenAI) solutions.
The role involves designing, building, and optimizing AI/ML systems leveraging Large Language Models (LLMs), RAG pipelines, and multi-agent orchestration frameworks.
Candidates will contribute to prompt engineering, AI infrastructure, and integration of AI agents with external tools, cloud platforms, and enterprise data environments
Exposure to document processing, knowledge graphs, semantic search, and enterprise AI governance is highly desirable.

Roles and Responsibilities

Develop and deploy AI/ML solutions using Python and relevant AI/ML/NLP libraries
Implement and optimize RAG (Retrieval-Augmented Generation) pipelines
Apply prompt engineering best practices to improve LLM performance
Work with vector databases and similarity search to enhance AI solutions
Build and orchestrate multi-agent systems using frameworks such as LangChain, LangGraph, AutoGen, or similar
Utilize computer vision libraries (OpenCV, Pillow) for image-related AI tasks
Conduct document processing and information extraction from unstructured data
Integrate AI tools and frameworks like Hugging Face, Transformers, PyTorch, or TensorFlow into workflows

Required Qualification

Proficiency in Python and libraries relevant to AI/ML and NLP
Deep understanding of LLMs, including prompt tuning, fine-tuning, and deployment
Experience with prompt engineering best practices
Strong knowledge of vector databases and similarity search
Experience with RAG architecture design and optimization
Familiarity with LangChain, LangGraph, AutoGen, or similar agent frameworks
Working knowledge of computer vision libraries (OpenCV, Pillow, etc.)
Experience in document processing and information extraction from unstructured data
Knowledge of tools like Hugging Face, Transformers, PyTorch, or TensorFlow

Preferred Qualifications

Experience with LLM orchestration and tools like Flowise, LlamaIndex, or Haystack
Exposure to cloud platforms (AWS, Azure, GCP) and deployment tools (Docker, Kubernetes)
Experience with data privacy and AI model governance in enterprise environments
Background in knowledge graphs, semantic search, or metadata-driven search pipelines

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