Data Scientist

About Kyndryl

At Kyndryl, we design, build, manage, and modernize the mission-critical technology systems that the world depends on every day. We are committed to moving forward and pushing ourselves to build a more equitable, inclusive world for our employees, customers, and communities.

The Role: Data Scientist

We are seeking a skilled Data Scientist with 2-5 years of experience in Data Science and Machine Learning. This role offers a fantastic opportunity to play a key part in developing and implementing innovative Generative AI solutions. The ideal candidate will have practical experience in areas such as NLP, Generative AI, LLMs, MLOps, optimization techniques, and AI solution architecture. Familiarity with business processes in at least one domain (e.g., Finance, Telecom, Retail) is highly desirable to quickly grasp requirements and devise effective solutions.

You will need a deep understanding of AI technologies and proven experience in designing and implementing cutting-edge AI systems. Expertise in data engineering, DevOps, and MLOps practices will also be highly valued.

Responsibilities

• Develop and deploy Generative AI and LLM projects, ensuring strong alignment with business objectives.
• Contribute to the development of Generative AI solutions and AI Agent development.
• Design and deploy Proof of Concepts (POCs) and Points of View (POVs) across diverse industry verticals to showcase the potential of Generative AI applications.

Candidate Profile

Must-Have Qualifications

• Graduate or Postgraduate in Computer Science, Computer Engineering, or an equivalent field, with 2-5 years of experience in the IT industry.
• Minimum of 2 years of hands-on experience in a Data Scientist role or a similar capacity.
• A strong understanding of Transformers, LLMs, Fine-Tuning, Agents, and RAG techniques.
• Comfort and proficiency in developing AI Agents.
• Demonstrable experience in AI/ML, with a specific focus on Generative AI and Large Language Models.
• Proven track record of working with major cloud platforms, including Azure, GCP, and AWS.
• Understanding of deploying LLMs on cloud or on-premise environments and leveraging APIs to build industry-specific solutions.
• Fluency in English, both written and spoken, is essential.