Lead AI Engineer

About the role

The Opportunity
The AI & Data Science team at MassMutual is seeking an impact-driven Lead AI Engineer to join our high-performing, cross-functional team. In this role, you will lead the design, deployment, and production scaling of advanced AI solutions that solve complex, high-value problems across the enterprise.

Responsibilities

• Architect, build, and lead end-to-end AI solutions supporting a range of enterprise use cases—from ideation through production deployment and monitoring—using LLMs, agentic AI, machine learning, and probabilistic modeling, with accountability for reliability, performance, and maintainability.
• Design and conduct rigorous evaluations of AI system performance, including experimentation, benchmarking across foundation models, and quantitative analysis, to validate approaches and inform technical decisions.
• Drive innovation by identifying emerging technologies, translating cutting-edge research into practical applications, and establishing team-wide best practices in AI development and responsible AI deployment.
• Build rapid prototypes to test and validate AI approaches and deliver production-grade AI-powered applications (e.g., intelligent interfaces, dashboards, automated workflows) when solutions prove viable.
• Collaborate with engineering teams to build robust, production-grade AI pipelines and APIs that integrate into the broader enterprise technology ecosystem.
• Influence senior leadership by aligning AI initiatives with enterprise strategy and communicating complex technical concepts and findings in clear, actionable terms.
• Mentor and develop junior talent, fostering a culture of technical excellence, scientific rigor, and continuous learning across the team.

Requirements

• 7+ years of experience in data science, machine learning, or AI engineering, with a track record of delivering impactful AI/ML solutions at scale.
• Deep expertise in machine learning, statistics, NLP, and LLMs, including generative AI, agentic architectures, prompt engineering, and LLM evaluation across a variety of foundation models and benchmarks.
• Demonstrated ability to build, deploy, and scale production AI systems from architecture planning through orchestration, monitoring, and end-user delivery.
• Strong programming skills in Python, with the ability to write clean, well-tested, production-quality code, including familiarity with Docker, Kubernetes, and other orchestration and deployment frameworks.
• M.S. or Ph.D. in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, Physics, or a related quantitative field.

Ideal Qualifications

• Familiarity with agentic AI tooling ecosystems, such as Bedrock AgentCore, AWS Strands, Azure, and MCP/A2A protocols.
• Experience developing and evaluating AI systems in a regulated industry, with a strong understanding of compliance and privacy standards.
• Breadth across AI and data science methods—including classical ML, causal inference, optimization, and Bayesian approaches—with comfort moving across techniques as problems demand.
• Proficiency in SQL and database design; familiarity with cloud-native data platforms, vector databases, and semantic search.
• Exceptional ability to translate complex AI concepts and quantitative findings into clear insights for non-technical stakeholders and senior leadership.
• Exceptional research credentials, such as published work, significant open-source contributions, or a strong record of scientific rigor applied in industry.

What to Expect as Part of MassMutual and the Team

• Regular meetings with the AI & Data Science team
• Focused one-on-one meetings with your manager
• Networking opportunities including access to Asian, Hispanic/Latinx, African American, women, LGBTQIA+, veteran and disability-focused Business Resource Groups
• Access to learning content on Degreed and other informational platforms
• Your ethics and integrity will be valued by a company with a strong and stable ethical business with industry leading pay and benefits

Pay

$172,000 - $225,700

Schedule

Hybrid model with at least three days per week in-person and remote Fridays, company-wide remote weeks, and a bank of flexible remote weeks to use throughout the year.