Associate – AI, ML Engineer

Role Overview

• 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
• 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
• Apply sound practices around feature engineering, hyperparameter tuning, cross-validation, and model selection to ensure robust, generalizable solutions
• Partner with data engineering teams to source, clean, and transform structured and semi-structured datasets
• Conduct thorough exploratory data analysis to surface patterns, anomalies, and opportunities that inform both modeling strategy and business decisions
• Apply appropriate evaluation metrics — such as RMSE, MAE, AUC-ROC, precision-recall, F1, R² and lift curves — to assess model performance in context
• Leverage model explainability techniques (e.g., SHAP values, partial dependence plots) to communicate findings clearly to both technical and non-technical audiences
• Collaborate with engineering and MLOps teams to package and deploy models into production environments
• Establish monitoring frameworks to track model drift, data quality issues, and performance degradation over time and lead remediation efforts when needed
• Translate complex analytical findings into clear, compelling narratives for business stakeholders
• Contribute to project scoping discussions, help define success metrics, and proactively surface risks or limitations in proposed analytical approaches
• 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

Requirements

• 3 to 5 years of hands-on experience in data science or a closely related quantitative role
• Strong proficiency in Python, including libraries such as scikit-learn, pandas, NumPy, and matplotlib
• Experience in Domino Lab is a plus
• Demonstrated experience building and deploying regression and classification models in a business context
• Solid understanding of statistical fundamentals including probability, hypothesis testing, and model assumptions
• Experience working with SQL for data extraction and transformation
• Familiarity with version control using Git and collaborative development practices
• Strong written and verbal communication skills with the ability to present technical work to diverse audiences

Tech Stack

• Numpy
• Pandas
• Python
• Scikit-Learn
• SQL

Benefits

• Health insurance
• 401(k) matching
• Flexible work hours
• Paid time off
• Professional development opportunities