Lead AI Engineer

Lead AI Engineer

Mass Mutual's AI & Data Science team 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. You'll architect and deliver generative AI, agentic AI, and LLM-based systems by applying rigorous scientific methods, writing high-quality production code, and communicating results to senior leadership.

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.

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

The Impact

• 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.

The Minimum Qualifications

• 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.

The Ideal Qualifications

• Familiarity with agentic AI tooling ecosystems, such as Bedrock Agent Core, 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…