Artificial Intelligence Architect

2 days ago

Bengaluru, Karnataka, India beBeeDataScientist Full time ₹ 1,50,00,000 - ₹ 2,50,00,000

Job Overview

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As a seasoned expert in artificial intelligence and machine learning, you will be responsible for architecting end-to-end pipelines involving prompt engineering, vector databases, retrieval-augmented generation (RAG), and large language model (LLM) fine-tuning. This role requires a deep understanding of LLMs, transformers, fine-tuning techniques, and prompt engineering.

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Key Responsibilities:

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  • Design and implement scalable AI architectures for GenAI solutions, leveraging multimodal models and cloud-native infrastructure.\
  • Develop and maintain reusable components, best practices, and guidelines for rapid development and experimentation of GenAI systems.\
  • Collaborate with cross-functional teams to identify high-impact GenAI use cases across domains such as customer support, content generation, document understanding, and code generation.\
  • Support proof-of-concepts, pilots, and production deployments of GenAI models in secure and compliant environments.\
  • Work with MLOps and cloud teams to enable continuous delivery, monitoring, and governance of GenAI systems.
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Requirements:

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To succeed in this role, you should possess the following skills and qualifications:

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  • Deep expertise in machine learning, natural language processing (NLP), and deep learning architectures.\
  • Hands-on experience with LLMs, transformers, fine-tuning techniques, and prompt engineering.\
  • Proficient in Python programming, with knowledge of popular libraries and frameworks such as Hugging Face Transformers, LangChain, and PyTorch.\
  • Experience with vector databases and RAG pipelines.\
  • Strong understanding of cloud-native AI architectures, containerization, and API integration.
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Preferred Qualifications:

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The following qualifications are desirable but not required:

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  • Experience with multimodal models (text + image/audio/video).\
  • Knowledge of AI governance, ethical AI, and compliance frameworks.\
  • Familiarity with MLOps practices for GenAI, including model versioning, drift detection, and performance monitoring.

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