AI Engineer

Overview

AI Engineer | Data Science & AI Engineering
· Full-Time Hybrid (3 days/week in office)

The Opportunity Mass Mutual’s AI & Data Science team is seeking a curious, motivated AI Engineer to join our high-performing, cross-functional team. In this role, you will contribute to the design, development, and evaluation of AI and machine learning solutions that address complex, high-value business problems. You’ll work on well-defined problems and progressively take on greater ownership as you build your technical skills and judgment, supported by a team of experienced practitioners who are invested in your growth.

This is a role with real responsibility from day one and a clear path to deeper autonomy as you develop.

This is a unique opportunity to work alongside experts in applied AI, statistics, and computer science. The team operates at the intersection of cutting-edge research and enterprise delivery, building AI solutions that shape the future of Mass Mutual and the life insurance industry  partner closely with technology and business stakeholders across the organization, and we invest in growth through a culture of peer learning, candid feedback, and shared technical standards.

The team is defined by a shared commitment to scientific and engineering excellence, meaningful work, and the kind of collaboration that makes challenging problems tractable.

• Develop, evaluate, and deploy AI and ML models and systems in support of defined business use cases, including LLM-based, generative and agentic AI, and classical machine learning approaches.
• Apply generative AI and agentic techniques (e.g., LLMs, RAG, prompt engineering) to real business problems, building and iterating on solutions under the guidance of senior practitioners.
• Conduct experiments and quantitative analyses to evaluate model and system performance, contributing to rigorous, evidence-based technical decisions.
• Build prototypes and contribute to production delivery of AI-powered applications, developing familiarity with the full lifecycle from ideation through deployment.
• Apply and grow proficiency in best practices for AI development, responsible AI, reproducibility, and production engineering standards.
• Communicate findings clearly to technical peers and stakeholders, translating analytical results into understandable and actionable terms.
• Contribute to team knowledge through documentation, peer feedback, and active participation in a collaborative, learning-oriented environment.

• Bachelor’s degree in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, Physics, Data Science, Artificial Intelligence, Machine Learning, or a related quantitative field with related experience in data science or AI concepts.
• Experience applying data science, machine learning, artificial intelligence, or AI engineering concepts through coursework, research, internships, capstone projects, labs, personal projects, competitions, or other applied learning.
• Exposure to machine learning, statistics, natural language processing, generative AI, or LLM-based approaches.
• Experience programming in Python, with the ability to write functional, readable code and continue developing coding practices.
• Understanding of the AI/ML development workflow, including data preparation, model training, evaluation, experimentation, and basic deployment concepts.

• 1+ years professional experience in data science, machine learning, artificial intelligence, AI engineering, software engineering, analytics, or a related technical field.
• Hands-on exposure to LLM tooling or agentic AI frameworks through coursework, personal projects, or professional experience.
• Experience with SQL and working with structured data; familiarity with cloud platforms or data infrastructure is a plus.
• Demonstrated intellectual curiosity and initiative, such as independent projects, open-source contributions, published or presented research, or participation in competitions.
• Clear written and verbal communication skills, with the ability to document work thoroughly and present findings to a technical audience.