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AI Solutions Architect

3 weeks ago

Chennai, Tamil Nadu, India beBeeMACHINELEARNING Full time US$ 1,50,000 - US$ 1,90,000
Deep Learning Solutions Architect

Are you passionate about developing and deploying cutting-edge AI solutions?

This pivotal role involves leading the entire lifecycle of AI models, from research and experimentation to robust production deployment.

You will tackle complex challenges across various domains, including Computer Vision, Natural Language Processing, and Generative AI applications.

Key Responsibilities
  • End-to-End Model Development: Lead the complete lifecycle of deep learning models, from initial data exploration and model design to training, rigorous evaluation, optimization, and deployment into production environments.
  • Advanced ML/DL Application: Design, develop, and implement state-of-the-art machine learning and deep learning algorithms for diverse applications, with a strong focus on Computer Vision and Natural Language Processing.
  • Generative AI Expertise: Drive the integration and development of Generative AI solutions, leveraging frameworks and techniques such as LangChain, LangGraph, Retrieval Augmented Generation (RAG), and advanced embedding strategies to create innovative capabilities.
  • Multi-Modal Solutions: Develop and deploy multi-modal AI solutions, including expertise in image embedding and fusing information from various data types.
Qualifications
  • Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Electrical Engineering, or a related quantitative field.
  • Proven professional experience as a Data Scientist or Machine Learning Engineer, with a significant focus on deep learning.
  • Demonstrated experience in the end-to-end training, evaluation, and deployment of deep learning models in a production setting.
  • Expertise in classical Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing.
  • Hands-on experience with Generative AI concepts and tools, including LangChain, LangGraph, RAG, and various embedding techniques.