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Lead AI Engineer

Job in Springfield, Hampden County, Massachusetts, 01104, USA
Listing for: Massachusetts Mutual Life Insurance
Full Time position
Listed on 2026-07-01
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist
Job Description & How to Apply Below

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, collaborating on initiatives that shape the future of Mass Mutual and the life insurance industry  ideal candidate brings deep expertise in machine learning and AI science, paired with the software engineering skills to take solutions from research prototypes to reliable, scalable production systems.

Overall 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 insights effectively.
  • Mentor and develop junior talent, fostering a culture of technical excellence, scientific rigor, and continuous learning.
Candidate Qualifications — Required
  • 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 of 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. Familiarity with Docker, Kubernetes, and other orchestration and deployment frameworks.
  • Exceptional communication skills, with the ability to translate complex AI concepts and quantitative findings into clear insights for non-technical stakeholders and senior leadership.
  • Education - M.S. or Ph.D. in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, Physics, or a related quantitative field. Candidates with exceptional research credentials—such as published work, significant open-source contributions, or a strong record of scientific rigor applied in industry—are strongly encouraged to apply.
Candidate Qualifications — Preferred
  • Familiarity with agentic AI tooling ecosystems, such as Bedrock Agent Core, AWS Strands, Azure, and MCP/A2A protocols.
  • Experience with the development and evaluation of AI systems in a regulated industry, exceeding compliance standards for AI and privacy.
  • Breadth across AI and data science methods, including traditional 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.
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