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AI Engineer -( IBM )TBD -788

eTeam APAC · HK

Weak hiring record · median 651 days open

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Open 207 days · How we score

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Job description -

  • Develop and maintain AI application and ML models for tasks including natural language processing, computer vision ensuring high performance and scalability, with a strong focus on building and optimizing Retrieval-Augmented Generation (RAG) applications using Azure AI Search or related tools for efficient information retrieval and indexing.
  • Research, design, and implement agentic AI workflows, enabling autonomous agents to handle complex, multi-step tasks through tool integration and decision-making logic.
  • Explore and prototype latest AI and ML concepts, such as the Model Context Protocol (MCP), to enhance application interoperability and functionality.
  • Conduct performance tuning, A/B testing, and monitoring of AI and ML systems in production environments, deploying solutions on OpenShift for containerized orchestration.
  • Collaborate with software engineers to embed AI and ML capabilities into web /mobile applications, using Python for scripting, automation, and model development in prototype level
  • Document technical designs, contribute to code reviews, and maintain best practices in AI and ML.

Requirements / Qualifications

  • Bachelor's or Master's degree in Computer Science, Data Science, Electrical Engineering, or a related field.
  • 1+ years of professional experience in Data Science and Machine Learning or enterprise application development
  • Experience in AI engineering or software development with AI components is advantage.
  • Strong experience with cloud platforms (AWS, GCP, or Azure) for deploying AI and ML workloads, with specific hands-on knowledge of Azure AI Search and Copilot Studio for RAG and agentic implementations is a plus
  • Excellent problem-solving skills and ability to work in agile environments.
  • Strong communication skills, with the ability to explain complex AI and ML concepts to non-technical stakeholders.
  • Experience with vector databases or knowledge graphs for RAG is a plus.

Skills

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