AI Prompt Senior Engineer-IN

About the job

AI Prompt Senior Engineer-IN An AI Prompt Senior Engineer is a specialist in creating, enhancing, and optimizing large language models for specific tasks and intended outcomes. Key Responsibilities and Duties • Design, develop, and optimize prompts for Large Language Models (LLMs). • Collaborate with data scientists and play a role in defining, testing and optimizing prompts that guide our AI systems to generate accurate, informative and creative outputs. • Create and improve AI models and algorithms, as well as maintain prompt libraries to generate prompts for natural language processing (NLP) applications. • Stay abreast of most recent developments on large language models. Educational Requirements • University (Degree) Preferred Work Experience • 3+ Years Required; 5+ Years Preferred Physical Requirements • Physical Requirements: Sedentary Work Career Level 7IC About the Role We are looking for a curious, analytically rigorous, and collaborative Data Scientist to join our growing team. In this role, you will work closely with business stakeholders, data engineers, and product teams to transform raw data into actionable insights and production-ready machine learning solutions. You will own the full modeling lifecycle — from problem framing and data exploration through model development, validation, and deployment — with a primary focus on supervised learning applications including regression and classification. Modeling & Machine Learning You will develop, validate, and maintain supervised machine learning models including linear and logistic regression, decision trees, random forests, gradient boosting methods (XGBoost, LightGBM), and support vector machines. Data Preparation & Exploration You will partner with data engineering teams to source, clean, and transform structured and semi-structured datasets. Model Evaluation & Interpretation You will apply appropriate evaluation metrics — such as RMSE, MAE, AUC-ROC, precision-recall, F1, R² and lift curves — to assess model performance in context. Deployment & Monitoring You will collaborate with engineering and MLOps teams to package and deploy models into production environments. Stakeholder Engagement You will translate complex analytical findings into clear, compelling narratives for business stakeholders. Mentorship & Knowledge Sharing You will contribute to the team's collective growth by participating in code reviews, documenting your work thoroughly, and sharing learnings through internal presentations or knowledge repositories.