Data Scientist-Artificial Intelligence

Introduction

In this role, youll work in one of our IBM Consulting Client Innovation Centers (Delivery Centers), where we deliver deep technical and industry expertise to a wide range of public and private sector clients around the world. Our delivery centers offer our clients locally based skills and technical expertise to drive innovation and adoption of new technology.

Your Role And Responsibilities

• Scale an existing RAG code base for a production grade AI application Requirements: Proficiency in Prompt Engineering, LLMs, and Retrieval Augmented Generation Programming languages like Python or Java Experience with vector databases
• Experience using LLMs in software applications, including prompting, calling, and processing outputs
• Experience with AI frameworks such as LangChain
• Troubleshooting skills and creative in finding new ways to leverage LLM Experience with Azure
  
  

• Strong programming skills, with proficiency in Python and experience with AI frameworks such as TensorFlow, PyTorch, Keras or Hugging Face. Understanding in the usage of libraries such as SciKit Learn, Pandas, Matplotlib, etc. Familiarity with cloud platforms (e.g. Kubernetes, AWS, Azure, GCP) and related services is a plus.
• Experience and working knowledge in COBOL & JAVA would be preferred
• Having experience in Code generation, code matching & code translation Prepare the effort estimates, WBS, staffing plan, RACI, RAID etc. .
• Excellent interpersonal and communication skills. Engage with stakeholders for analysis and implementation. Commitment to continuous learning and staying updated with advancements in the field of AI.
• Demonstrate a growth mindset to understand clients business processes and challenges
  
  

• Education: Bachelors, Masters, or Ph.D. degree in Computer Science, Artificial Intelligence, Data Science or a related field.
• Ability to communicate with internal and external clients to understand and define business needs, providing analytical solutions
• Ability to communicate results to technical and non-technical audiences