Sr. AI/ML Engineer

TIAA•Iselin, NJ

1d•$150,000 - $209,000•Onsite

About The Position

Nuveen, the investment manager of TIAA, offers a comprehensive range of outcome-focused investment solutions designed to secure the long-term financial goals of institutional and individual investors. Its affiliates offer deep expertise across a comprehensive range of traditional and alternative investments through a wide array of vehicles and customized strategies. The senior AI/ML Engineer is a key technical leader and major contributor to a high-performing, fast-paced engineering team responsible for designing, building, and deploying enterprise-grade generative AI solutions. This role requires deep expertise in distributed systems, scalable architecture, and cutting-edge AI/ML technologies, with a focus on delivering production-ready applications on AWS using Python. As a hands-on technical leader, you will be involved in the full software development lifecycle (SDLC), from requirements gathering and architecture design through implementation, deployment, and ongoing optimization. You will design robust, low-latency AI applications, implement best practices in DevOps and MLOps, and ensure all solutions meet enterprise standards for security, governance, and compliance.

Requirements

5+ years of software engineering experience with demonstrated progression in technical leadership and system design
3+ years of hands-on experience with AI/ML, with at least 1+ year focused on Generative AI, LLMs, and production deployment
Extensive AWS experience with hands-on implementation of compute, storage, networking, security, and AI/ML services.
Expert-level Python programming with deep knowledge of advanced language features, design patterns, and performance optimization.
Full-stack development skills including backend API development with RESTful design principles, frontend development
Production experience with Generative AI technologies: LLM APIs, RAG frameworks and vector databases, Prompt engineering and optimization techniques, AI agent frameworks, Model fine-tuning and evaluation
Experience in building CI/CD pipelines using Infrastructure as Code (Terraform, CloudFormation), Container orchestration (Docker, Kubernetes/EKS), Monitoring and observability tools
Understanding of distributed systems, microservices architecture, event-driven design, and scalability patterns

Nice To Haves

Experience with additional cloud platforms (Azure, GCP) and multi-cloud architectures
Contributions to open-source AI/ML projects or published research
Knowledge of additional programming languages (C++, Go, Rust)
Experience with real-time streaming and event-driven architecture
Familiarity with advanced AI techniques (multimodal models, vision transformers, diffusion models)

Responsibilities

Design and implement generative AI solutions using comprehensive pipelines.
Build end-to-end systems integrating structured and unstructured data stores, graph databases, indexing strategies, and reinforced learning strategies, embedding models, and LLMs to enable context-aware, knowledge-grounded responses.
Build production-grade AI agents using both low-code platforms and high-code custom implementations and optimize for performance and maintainability.
Design, train, and deploy end-to-end machine learning pipelines encompassing data ingestion, feature engineering, model selection, hyperparameter tuning, and validation using frameworks such as scikit-learn, XGBoost, PyTorch, and TensorFlow.
Apply advanced statistical modeling, exploratory data analysis, and feature store design to extract actionable insights from large, complex, and heterogeneous datasets.
Build and maintain robust data preprocessing and transformation workflows to handle class imbalance, missing data, and distribution drift, and implement model monitoring pipelines to detect and respond to concept drift, data quality degradation, and performance regression in production environments, ensuring sustained model reliability and business relevance over time.
Architect and develop large-scale, cloud-native (AWS) Python applications using modern frameworks, optimized for high performance, low latency, and horizontal scalability.
Implement Infrastructure as Code (IaC) using Terraform for version-controlled, reproducible infrastructure provisioning.
Implement robust testing strategies including unit tests, integration tests, end-to-end tests, performance tests, and AI-specific testing (prompt regression tests, model output validation).
Apply security best practices including IAM, least-privilege access, role-based access control (RBAC), and multi-factor authentication; enforce encryption at rest and in transit; and ensure secure key management and data masking/tokenization for sensitive information.
Design and manage containerized applications using Docker for packaging and Kubernetes (EKS) or ECS for orchestration, ensuring efficient resource utilization and auto-scaling.
Establish and enforce CI/CD best practices using GitHub Actions, Jenkins, GitLab CI, or AWS CodePipeline to automate build, test, and deployment processes.
Maintain comprehensive documentation of system architectures, data flows, security controls, and operational procedures to support compliance audits and knowledge transfer.