AI Engineer

The Opportunity

The AI & Data Science team at MassMutual is seeking an AI Engineer to develop and deliver AI solutions that address complex business problems. This role involves applying machine learning, generative and agentic AI, and LLM-based techniques to real-world challenges.

Team

This role offers the chance 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 MassMutual and the life insurance industry.

The Impact

• Design, build, and deliver end-to-end AI/ML solutions for defined business use cases.
• Frame and scope AI problems independently, defining success metrics and evaluation criteria.
• Build rapid prototypes to test AI approaches and advance solutions into production-grade applications.
• Communicate findings and recommendations clearly to technical peers and non-technical stakeholders.

The Minimum Qualifications

• 4+ years of experience in data science, machine learning, or AI engineering.
• Experience with machine learning, statistics, NLP, and LLMs, including generative AI, agentic architectures, prompt engineering, and evaluation of LLM performance.
• Experience building and deploying production AI systems, including model integration, API development, and cloud-based infrastructure.
• Bachelor’s degree in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, Physics, or a related quantitative field.

The Ideal Qualifications

• Experience with agentic AI frameworks and tooling, such as Bedrock AgentCore, AWS Strands, Azure, and MCP/A2A protocols.
• Breadth across AI and data science methods, including classical ML, causal inference, optimization, and Bayesian approaches.
• Proficiency in SQL and database design; familiarity with cloud-native data platforms, vector databases, and semantic search.
• Master’s degree or equivalent depth demonstrated through research, applied projects, or prior work.
• Candidates with a Master’s may be considered with fewer years of professional experience.
• Applied research credentials, such as published work, significant open-source contributions, or a demonstrated record of scientific rigor in industry.
• Clear and effective communication skills, with the ability to explain technical concepts and present findings to both technical and non-technical audiences.