Senior AI Engineer (Hybrid) — LLMs & Agentic AI

MassMutual; Boston (MA); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Networking opportunities

Learning content access

Job summary

MassMutual is seeking an AI Engineer to join the Data Science & AI Engineering team in a full-time hybrid role (3 days in office). You will own the design, development, and deployment of AI solutions across the enterprise, applying ML, generative AI, LLMs, and probabilistic modeling to address high-value business problems.

You will frame problems, define success metrics with stakeholders, conduct rigorous evaluations, and deliver production-grade pipelines and interfaces.

Qualifications

• 4+ years of data science, ML, or AI engineering experience delivering AI/ML solutions independently and at scale.
• Experience with ML, NLP, LLMs, including generative AI, agentic architectures, prompt engineering, and evaluation of LLM performance.

Responsibilities

• Design, build, and deliver end-to-end AI/ML solutions for defined business use cases with ownership from development to deployment.
• Frame and scope AI problems independently, define success metrics with stakeholders, and guide evaluation.
• Build production-grade AI systems with model integration, API development, and cloud-based infra.
• Communicate findings clearly to technical and non-technical stakeholders and share knowledge with the team.

Skills

Data science

Machine learning

NLP & LLMs

Python

Production AI systems

API development

Cloud computing

Education

Bachelor's degree in Computer Science or related

Tools

Bedrock AgentCore

AWS

Azure

SQL

Vector databases

Job description

MassMutual is seeking an AI Engineer to join the Data Science & AI Engineering team in a full-time hybrid role (3 days in office). You will own the design, development, and deployment of AI solutions across the enterprise, applying ML, generative AI, LLMs, and probabilistic modeling to address high-value business problems.

You will frame problems, define success metrics with stakeholders, conduct rigorous evaluations, and deliver production-grade pipelines and interfaces.