Associate - AI | ML Engineer

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.

This is an excellent opportunity for someone who has moved beyond foundational data science work and is ready to take on greater ownership of projects while continuing to grow their technical depth and business acumen.

Key Responsibilities
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. You will apply sound practices around feature engineering, hyperparameter tuning, cross-validation, and model selection to ensure robust, generalizable solutions.

Data Preparation & Exploration You will partner with data engineering teams to source, clean, and transform structured and semi-structured datasets. You will conduct thorough exploratory data analysis to surface patterns, anomalies, and opportunities that inform both modeling strategy and business decisions.

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. You will leverage model explainability techniques (e.g., SHAP values, partial dependence plots) to communicate findings clearly to both technical and non-technical audiences.

Deployment & Monitoring You will collaborate with engineering and MLOps teams to package and deploy models into production environments. You will establish monitoring frameworks to track model drift, data quality issues, and performance degradation over time, and will lead remediation efforts when needed.

Stakeholder Engagement You will translate complex analytical findings into clear, compelling narratives for business stakeholders. You will contribute to project scoping discussions, help define success metrics, and proactively surface risks or limitations in proposed analytical approaches.

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.

Required Qualifications
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
Preferred Qualifications
Experience with cloud platforms such as AWS, Azure, or GCP and their respective ML services
Familiarity with MLflow, Kubeflow, or similar experiment tracking and model registry tools
Exposure to imbalanced classification problems and techniques such as SMOTE or cost-sensitive learning
Experience with time series regression or survival analysis
Background in financial services, insurance, healthcare, or other regulated industries
Bachelor's or advanced degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field

Related SkillsBusiness Acumen, Data Preprocessing, Data Science, Innovation, Machine Learning (ML), Market/Industry Dynamics, Predictive Modeling, Programming, Statistics

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Company Overview

TIAA Global Capabilities was established in 2016 with a mission to tap into a vast pool of talent, reduce risk by insourcing key platforms and processes, as well as contribute to innovation with a focus on enhancing our technology stack. TIAA Global Capabilities is focused on building a scalable and sustainable organization , with a focus on technology , operations and expanding into the shared services business space.

Working closely with our U.S. colleagues and other partners, our goal is to reduce risk, improve the efficiency of our technology and processes and develop innovative ideas to increase throughput and productivity.

We are an Equal Opportunity Employer. TIAA does not discriminate against any candidate or employee on the basis of age, race, color, national origin, sex, religion, veteran status, disability, sexual orientation, gender identity, or any other legally protected status.

Our Culture of Impact

At TIAA, we're on a mission to build on our 100+ year legacy of delivering for our clients while evolving to meet tomorrow's challenges. We equip our associates with future-focused skills and AI tools that enable us to advance our mission. Together, we are fighting to ensure a more secure financial future for all and for generations to come. We are guided by our values: Champion Our People, Be Client Obsessed, Lead with Integrity, Own It, and Win As One. They influence every decision we make and how we work together to serve our clients every day. We thrive in a collaborative in-office environment where teams work across organizational boundaries with shared purpose, accelerating innovation and delivering meaningful results. Our workplace brings together TIAA and Nuveen's entrepreneurial spirit, where we work hard and work together to create lasting impact. Here, every associate can grow through meaningful learning experiences and development pathways—because when our people succeed, our impact on clients' lives grows stronger.