2027 Technology, Data, AI & Ventures Summer Internship Program - AI Engineer (MLOps) Intern

Within the Tech, Data, AI, Ventures (TDAV) organization, our work is guided by a shared vision: deploying the power of technology, data, AI and ventures to accelerate sustainable competitive advantage for New York Life's businesses. We build solutions that power how we serve policy owners, agents, advisors and employees. Grounded in New York Life’s culture of long-term commitment, integrity and putting people at the center of what we do, our work reflects a purpose-driven approach to innovation.

We engineer complex systems that translate into measurable business outcomes. Across technology, data, AI, cyber, product, digital experience, architecture and infrastructure, TDAV combines the scale and investment of an industry leader with the opportunity to work with leading-edge technologies and help shape how a world-class financial services company competes in the AI era — all backed by the stability and purpose of a mutual company built to last.

Shape your future with a dynamic internship experience at New York Life. Your internship journey is designed to challenge you through hands-on work experience that will equip you with valuable skills you can use anywhere. You will build your network through collaboration and connection with talented interns and experienced employees through team-building activities, a collaborative intern capstone, and fun social events. By the end of your internship, you'll be equipped with new skills and a network that will propel you forward in your career journey.

What You'll Do: 

Focus on building, training, and deploying AI systems and infrastructure. You'll design, implement, and optimize machine learning and Generative AI models in production environments. This work is more engineering-oriented, emphasizing software development, scalability, and integration. As an intern, you will support the development and enhancement of New York Life’s machine learning and AI capabilities and help bring innovative AI solutions into production, from refining models to developing intelligent applications and automation solutions. Example projects include designing and testing new patterns for model deployment using Python APIs, improving MLOps and DevOps platforms through dashboards and monitoring tools like New Relic, and contributing to automation frameworks that ensure model reliability and performance at scale.

Required Skills: 

• Undergraduate or Graduate student pursuing a degree in Data Science, Computer Science, Artificial Intelligence, Statistics or a related technical field.
• Proficiency in Python, familiarity with APIs, and a solid understanding of automated testing and DevOps tools (e.g., EKS, CI/CD, New Relic)

Eligibility Criteria: 

• Currently pursuing a bachelor's or master's degree with an expected graduation date of December 2027 or May 2028
• Available to work full-time for the entire duration of the 11-week Summer Internship Program (May to August)
• Able to work in a hybrid environment, with three days per week on-site in New York City
• Passionate about technology, data, AI, and innovation, with a collaborative mindset and a desire to learn and make an impact

To be eligible for this program, you must be authorized to work in the U.S. We do not offer any type of employment-based immigration sponsorship for this program. Likewise, this program is not available to those authorized to work under optional practical training (OPT) or curricular practical training (CPT). 

As part of the Technology, Data & Analytics Ventures (TDAV) Internship Program, this posting represents one of seven specialized internship opportunities: AI Engineer (MLOps), Software Engineer, Data Engineer, Platform Engineer, Security Engineer, Data Scientist, and Analyst. We encourage candidates to explore all available TDAV internship postings and apply for the role(s) that best align with their skills, interests, and career aspirations.

Curious what it’s like to intern at NYL? Check out #NYLEarlyCareers on LinkedIn!

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