Associate – AI, ML Engineer
TIAA
full-time
Posted on:
Location Type: Hybrid
Location: Pune • India
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Tech Stack
About the role
- 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
Benefits
- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
- Professional development opportunities
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Large Language Modelsnatural language processingmachine learninglinear regressionlogistic regressiondecision treesrandom forestsgradient boostingfeature engineeringmodel evaluation
Soft Skills
collaborationcommunicationproblem-solvinganalytical thinkingproject scopingdocumentationpresentation skillsteamworkrisk assessmentnarrative translation