GenAI Lead

• Architect and develop

  end to end Gen AI

  and machine learning solutions, focusing on scalability, performance, and modularity, in collaboration with DDIT teams to ensure alignment with enterprise architecture standards.
• Design, train, and fine tune

  Large Language Models (LLMs) like GPT, Claude, or Cohere for various applications or pilots, working closely with DDIT to leverage existing data infrastructure and tools

  .
• Optimize

  AI systems for

  deployment on cloud platforms such as AWS, Azure, Snowflake, or Databricks

  , ensuring high performance and cost efficiency, in partnership with DDIT to utilize best practices and cloud resources.

• Develop APIs

  and

  integrate AI models into existing enterprise systems

  for seamless business adoption, coordinating with DDIT to ensure compatibility and security.
• Collaborate with data scientists, software engineers, IDS colleagues, DDIT, and business teams to turn complex requirements into pilot AI solutions, ensuring cross functional alignment and support.
• Stay updated on the latest advancements in AI, especially in LLM architectures, generative AI, and model optimization techniques, and share insights with DDIT to foster continuous improvement and innovation.
• Conduct technical reviews, ensure code quality, and enforce best practices in AI development and deployment, in collaboration with DDIT to maintain high standards and compliance.
• Maintain the analytics workbench along with the data science team, ensuring integration with DDIT's data management and analytics platforms

Additional qualifications are as follows:

• Advanced degree in Computer Science, Engineering, or a related field (PhD preferred).

• 5+ years of experience in AI/ML engineering

  (data engineering could be appropriate depending on experience), with

  at least 2 years focusing on designing and deploying LLM based solutions

  .
• Strong proficiency in building AI/ML architectures and deploying models at scale.
• Deep knowledge of

  LLM sand

  experience in fine tuning and applying them in business contexts.
• Knowledge of containerization technologies (Docker, Kubernetes) and CI/CD pip