Agentic AI Engineer (Insurance & Agentic Workflows) Hybrid, Boston or WebsterSummaryAre you excited about building AI systems that solve real-world problems at scale? We are looking for a hands-on engineer to design, build, and ship production-grade AI agents that transform complex, high-impact insurance workflows. You will own solutions end to end, from early prototypes to reliable production systems, and help define how agentic AI is engineered and scaled across the organization. Working at the intersection of AI, software engineering, and insurance, you will tackle real business problems, collaborate directly with technical and domain experts, and see the measurable impact of what you build.
This role requires strong software engineering depth in Python, and practical experience building LLM-based and agentic systems. Experience in insurance or another regulated industry is a big plus.
What You'll Do- Design, build, and deploy AI agents and multi-step agentic workflows that transform high-impact complex insurance processes such as underwriting, and claims, creating reusable components and engineering patterns that accelerate future solutions
- Translate business and solution designs defined with the AI architect/product owner into working agents, advising on the technical feasibility, cost, and reliability trade-offs between deterministic rules, traditional ML, plain GenAI, and autonomous agents
- Build agents as well-defined backend services (e.g., FastAPI) with stable API contracts, secure authentication, resilient error handling, and automated testing, integrating them with enterprise systems, data sources, and APIs through tools and MCP servers
- Own agent quality and continuous improvement by defining success criteria, building test datasets and automated evaluations, diagnosing failures, and implementing targeted improvements
- Implement guardrails, human-in-the-loop checkpoints, action limits, observability, monitoring performance, reliability, cost control, and audit logging in line with AI governance and regulatory requirements
- Work directly with business and technical teams to rapidly prototype (building lightweight MVP interfaces (e.g., Streamlit)), validate, deploy, and scale solutions using cloud, containerization, and CI/CD practices.
- Measure technical performance and business value and communicate results and limitations to technical and non-technical stakeholders.
Required skills- Bachelor's degree in Computer Science, Engineering, or a related quantitative field, with either 7+ years of software, machine learning, or data engineering experience, or a Master's degree with 5+ years of relevant experience
- 2+ years of hands-on experience building LLM-based applications, including at least one agentic system (tool calling, multi-step workflows, etc.) deployed to production and used by real users
- Strong Python skills and solid software engineering fundamentals, including API and tool integration, automated testing, error handling, version control, and debugging of non-deterministic systems
- Hands-on experience with at least one agent framework or SDK (LangGraph strongly preferred) and with major model APIs or managed AI platform such as Amazon Bedrock, building production AI agents that integrate with tools, APIs, and enterprise systems.
- Experience evaluating LLM applications or agents, including test datasets, metrics, regression testing, and appropriate use of model-based evaluators.
- Understanding of agent reliability and security risks, combined with pragmatic judgment, strong problem-solving skills, and the ability to determine where an agentic approach is, and is not, appropriate, communicating effectively with technical and business stakeholders. Proven ability to solve ambiguous, complex problems with minimal direction and high autonomy, with a results-driven mindset and the persistence to find root causes
- Strong communication skills, with the ability to work closely with architects and business experts and explain agent behavior and limitations to non-technical stakeholders
Desired skills- Insurance domain knowledge is highly preferred, with claims or underwriting experience a strong plus
- Experience deploying production AI applications to the cloud, with AWS preferred, using containers and CI/CD
- Experience with retrieval-augmented generation (RAG), context engineering, and structured outputs, and with integrating agents into enterprise systems via APIs and MCP
- Experience mentoring team members and influencing best practices across teams
Salary Range: $121,000- $191,000
Benefits Highlights:At Mapfre, we invest in our employees' well-being by offering a comprehensive benefits package, including:
- Paid Time Off (PTO): A flexible PTO program that combines vacation, personal, volunteer and sick time into one convenient bank of paid time off.
- 401(k) & Profit Sharing: Eligible employees may participate in Mapfre's 401(k) plan that includes company matching contributions up to certain amounts under the terms of the plan.
- Medical, Dental and Vision Coverage: Choice of comprehensive medical, dental and vision insurance coverage under group plans offered by the company.
- Other Benefits Include: STD, LTD, Life Insurance, FSA, HSA and various wellness programs
Benefits provided are subject to the terms and conditions of the applicable benefit plan or policy. The Company reserves the right to alter, amend, or terminate benefits in its sole discretion. To learn more about our benefit offerings please visit:https://mapfre.gobenefits.net/
