Data Scientist-Artificial Intelligence

Introduction

A career in IBM Consulting is rooted by long-term relationships and close collaboration with clients across the globe. You'll work with visionaries across multiple industries to improve the hybrid cloud and AI journey for the most innovative and valuable companies in the world. Your ability to accelerate impact and make meaningful change for your clients is enabled by our strategic partner ecosystem and our robust technology platforms across the IBM portfolio

Your Role And Responsibilities

An AI Data Scientist at IBM is not just a job title - it’s a mindset. You’ll leverage the watsonx,AWS Sagemaker,Azure Open AI platform to co-create AI value with clients, focusing on technology patterns to enhance repeatability and delight clients.

We are seeking an experienced and innovative AI Data Scientist to be specialized in foundation models and large language models. In this role, you will be responsible for architecting and delivering AI solutions using cutting-edge technologies, with a strong focus on foundation models and large language models. You will work closely with customers, product managers, and development teams to understand business requirements and design custom AI solutions that address complex challenges. Experience with tools like Github Copilot, Amazon Code Whisperer etc. is desirable.

Success is our passion, and your accomplishments will reflect this, driving your career forward, propelling your team to success, and helping our clients to thrive.

Day-to-Day Duties

• Proof of Concept (POC) Development: Develop POCs to validate and showcase the feasibility and effectiveness of the proposed AI solutions. Collaborate with development teams to implement and iterate on POCs, ensuring alignment with customer requirements and expectations.
• Help in showcasing the ability of Gen AI code assistant to refactor/rewrite and document code from one language to another, particularly COBOL to JAVA through rapid prototypes/ PoC
• Documentation and Knowledge Sharing: Document solution architectures, design decisions, implementation details, and lessons learned. Create technical documentation, white papers, and best practice guides. Contribute to internal knowledge sharing initiatives and mentor new team members.
• Industry Trends and Innovation: Stay up to date with the latest trends and advancements in AI, foundation models, and large language models. Evaluate emerging technologies, tools, and frameworks to assess their potential impact on solution design and implementation
  
  

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• 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 leveraging LLM capabilities would be a Big plus (e.g. Amazon Code Whisperer, Github Copilot etc.)
• Soft Skills: 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.
• Growth mindset: Demonstrate a growth mindset to understand clients' business processes and challenges, experience in python and pyspark will be added advantage
  
  

Preferred Technical And Professional Experience

• Experience: Proven experience in designing and delivering AI solutions, with a focus on foundation models, large language models, exposure to open source, or similar technologies. Experience in natural language processing (NLP) and text analytics is highly desirable. Understanding of machine learning and deep learning algorithms.
• Strong track record in scientific publications or open-source communities
• Experience in full AI project lifecycle, from research and prototyping to deployment in production environments