Senior Associate - AI Engineer

New York Life • New York, NY

3d • $100,000 - $143,000 • Hybrid

About The Position

As part of New York Life’s Artificial Intelligence & Data organization, the Senior Associate - AI Engineer contributes to the company’s digital transformation by developing and deploying AI and machine learning solutions that improve efficiency and enhance client, agent, and employee experiences. The role supports solutions spanning traditional machine learning, Generative AI, and Agentic AI, with an emphasis on building technical skills, learning enterprise AI practices, and delivering defined components of AI solutions. The engineer collaborates with Data Scientists, Engineers, and product and technology partners while developing knowledge of model lifecycle management, responsible AI principles, and New York Life’s technology ecosystem.

The position contributes to the full model lifecycle, from data exploration and feature engineering through training, validation, deployment, monitoring, and iteration. It involves building and evaluating models, applying statistical methods and experimentation, and helping operationalize solutions on cloud and modern data platforms. The role also supports agentic and LLM/RAG solutions, making AI capabilities available through APIs, dashboards, and workflow applications, and prototyping lightweight user interfaces. Work is conducted in partnership with business, technology, product, data, and MLOps teams, following governance, documentation, testing, and CI/CD practices.

This is a hybrid position with three days per week in the designated workplace. The listed salary range is $100,000–$143,000. The role is exempt from overtime and eligible for a discretionary annual bonus; it is not sales-bonus eligible. Actual base salary depends on factors including experience, skills, qualifications, and job location.

Requirements

• Advanced degree in Computer Science, Data Science, Machine Learning, AI, Engineering, Mathematics, Statistics, or a related quantitative field.
• At least 3 years of hands-on experience building and deploying ML models to solve real-world business problems, including feature engineering, model selection, validation strategies, and performance monitoring.
• Strong foundation in statistics and machine learning, including supervised and unsupervised methods, probabilistic modeling, and model interpretability such as SHAP, with the ability to select and evaluate appropriate approaches.
• Proficiency in Python and SQL.
• Working knowledge of core software engineering concepts, including version control with Git/GitHub, testing, and documentation.
• Working knowledge of Generative AI concepts, such as LLMs, prompt engineering, and RAG.
• Exposure to Agentic AI architectures, such as tool-using agents and orchestration frameworks.
• Ability to partner with business stakeholders and communicate model design and outcomes to technical and non-technical audiences.
• Strong communication skills and a collaborative mindset.
• Proactive mindset and commitment to continuous learning.

Nice To Haves

• Experience in the life insurance industry or consumer finance domains.

Responsibilities

• Execute AI/ML initiatives in partnership with data, technology, product, and business teams.
• Deliver business-aligned OKRs and ensure AI initiatives are outcome-driven and tied to enterprise value creation.
• Contribute to the end-to-end model lifecycle, including data exploration, feature engineering, model training and validation, deployment, monitoring, and iteration.
• Design, build, and validate supervised and unsupervised ML models, considering model selection, evaluation strategies, and production readiness.
• Apply statistical methods and experimentation techniques, including hypothesis testing, causal inference, and A/B testing, to validate model performance and support evidence-based decisions.
• Develop and help operationalize models using AWS or GCP services and modern data platforms such as Snowflake and Databricks, ensuring quality, security, and scalability; partner with ML engineers on large-scale production deployment and scaling.
• Assist in designing and evaluating agentic and AI-powered solutions that automate routine business tasks with human-in-the-loop checkpoints, escalation thresholds, and safety guardrails.
• Assist in implementing and refining LLM/RAG approaches, including vector stores, embeddings, retrieval optimization, and prompt orchestration, with evaluators for reliability.
• Ensure AI capabilities are accessible through APIs, dashboards, and integrated workflow applications used by business teams.
• Build lightweight UI prototypes, such as Streamlit applications, to validate usability and support adoption.
• Follow model governance, documentation, testing, and CI/CD best practices in partnership with MLOps.

Benefits

• Full package of employee benefits, with offerings including leave programs, adoption assistance, and student loan repayment programs.
• Eligible for an annual discretionary bonus.
• May be eligible to participate in an incentive program.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level