Why join MassMutual
MassMutual offers a purpose-driven environment focused on helping people secure their future and protect those they love. Grounded in mutuality, the company aims to improve financial well-being for Americans and operates as a community that shares risk and resources. We cultivate a thriving, inclusive culture where everyone is valued, belongs, and can contribute. Our approach to work blends collaboration with flexibility through a hybrid model that prioritizes in-person interaction while enabling remote options.
Compensation, location, and qualifications
Location: Springfield, MA (hybrid) • Salary: USD 172,000 - 225,700 per year • Experience: 7+ years • Education: MS or PhD.
Benefits
- Paid Time Off
- Health & Well-Being
- Financial Well-Being
- Taking Care
- Giving Back
- Commuter Benefits
Benefits for the whole you
There is more to your life than your job and more to your goals than a paycheck. We take a holistic view of compensation and benefits that supports a healthy balance across work, family, and community. In addition to medical, dental, 401(k), and generous vacation time, we offer three paid volunteer days, a $1,250 annual Well-Being Wallet, and up to 320 hours of caregiver leave.
Technologies
Python, Docker, Kubernetes, Bedrock AgentCore, AWS Strands, Azure, MCP/A2A protocols, SQL, vector databases, semantic search, cloud-native data platforms.
Responsibilities
- Architect, build, and lead end-to-end AI solutions for enterprise use cases, from ideation through production deployment and monitoring, leveraging LLMs, agentic AI, machine learning, and probabilistic modeling with accountability for reliability and maintainability.
- Design and conduct rigorous evaluations of AI system performance, including experimentation, benchmarking across foundation models, and quantitative analysis to inform decisions.
- Drive innovation by identifying emerging technologies, translating research into practical applications, and establishing team-wide best practices in responsible AI development.
- Develop rapid prototypes to test approaches and deliver production-grade AI-powered applications such as intelligent interfaces, dashboards, and automated workflows when viable.
- Collaborate with engineering teams to build robust AI pipelines and APIs that integrate into the broader enterprise tech ecosystem.
- Influence senior leadership by aligning AI initiatives with enterprise strategy and communicating complex technical findings in clear, actionable terms.
- Mentor junior talent and foster 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 evaluation across a range of foundation models.
- Proven ability to build, deploy, and scale production AI systems from architecture through orchestration, monitoring, and end-user delivery.
- Strong Python programming skills with production-quality code and familiarity with Docker, Kubernetes, and related deployment frameworks.
- MS or PhD in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, Physics, or a related quantitative field.
- Familiarity with agentic AI tooling ecosystems such as Bedrock AgentCore, AWS Strands, Azure, and MCP/A2A protocols.
- Experience developing and evaluating AI systems in regulated environments with a strong understanding of compliance and privacy standards.
- Broad proficiency across AI and data science methods, including classical ML, causal inference, optimization, and Bayesian approaches, with comfort across techniques as problems require.
- Proficiency in SQL and database design; familiarity with cloud-native data platforms, vector databases, and semantic search.
- Ability to translate complex AI concepts and quantitative findings into clear insights for non-technical stakeholders and senior leadership.
- Strong research credentials, such as published work, significant open-source contributions, or a demonstrated record of scientific rigor in industry.
