GenAI

Major Accountabilities

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 LLMsand experience in fine tuning and applying them in business contexts.

Knowledge of containerization technologies (Docker, Kubernetes) and CI/CD pip