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Data Scientist/AI Engineer @ Square One Resources

Summary

Design, build, and deploy end-to-end AI/ML and NLP systems, including transformer models and customer-facing AI agents, using Python, AWS, and MLOps practices.

  • Delivering AI-powered, customer-facing solutions.
  • Design, develop, and deploy end-to-end machine learning and NLP systems.
  • Creating impactful AI products by working closely with both technical and business stakeholders.

  • Strong Python programming skills, including production-grade development.
  • Hands-on experience building and deploying end-to-end ML systems.
  • Practical experience with Machine Learning projects in commercial environments.
  • Experience developing AI agents or intelligent customer-facing applications.
  • Strong knowledge of NLP and transformer-based architectures (e.g. HuggingFace).
  • Experience working with customer feedback analysis and CX prediction models.
  • Good knowledge of AWS services, especially Amazon SageMaker, Amazon MWAA, and Amazon Athena.
  • Experience with scikit-learn and other ML frameworks.
  • Experience with Docker and ML deployment workflows.
  • Excellent communication skills and the ability to collaborate effectively with both technical and non-technical stakeholders.
  • Delivering AI-powered, customer-facing solutions.
  • Design, develop, and deploy end-to-end machine learning and NLP systems.
  • Creating impactful AI products by working closely with both technical and business stakeholders.
,( Design, build, and deploy end-to-end Machine Learning solutions, from experimentation to production. , Develop AI agents for customer-facing use cases. , Experiment with various ML techniques to identify the most effective approach for different business problems. , Design and implement NLP solutions, including transformer-based models. , Analyze customer feedback data and build Customer Experience (CX) prediction models. , Collaborate with cross-functional teams to translate business requirements into scalable AI solutions. , Support production deployment and maintenance of ML models using modern MLOps practices. ) Requirements: Machine learning, AI, Use cases, NLP, MLOps, Python, Hugging Face, AWS, MWAA, Athena, scikit-learn, Docker

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