The OpportunityWe're looking for a
hands-on Lead Agentic AI Solutions Architect to turn complex business needs into high-impact, production-ready Agentic AI solutions. You'll partner with business leaders and domain experts to identify where AI can create value and lead solutions from discovery through deployment and continuous improvement.
This is a
lead individual contributor role with end-to-end delivery accountability. You'll contribute production code, solve complex technical challenges, provide technical direction to Agentic AI Engineers, and partner across business, technology, data, governance, and platform teams. You'll also help shape our broader AI tools strategy, determining when enterprise capabilities, integrations, low-code/no-code solutions, or custom development are the right approach.
Success requires a combination of
technical depth, delivery leadership, stakeholder engagement, and business judgment. You'll bring structure to ambiguity, build strong partnerships, and keep cross-functional teams focused on measurable outcomes.
Insurance or regulated-industry experience is a plus.What You'll Do- Lead AI solutions from concept through production, translating business objectives into scope, priorities, milestones, and phased releases while managing dependencies, risks, blockers, deployment, and operational handoff.
- Partner with business and domain experts to understand and redesign insurance workflows, assess data, knowledge, integration, and process readiness, and identify prerequisites for successful delivery.
- Recommend the right solution for the problem, determining when agentic AI, machine learning, rules-based approaches, existing enterprise tools, or custom development provide the best balance of value, reliability, cost, risk, and human judgment.
- Architect secure, production-ready agentic AI solutions, including orchestration, tool and data integrations, context and state management, execution boundaries, recovery and fallback mechanisms, and human oversight.
- Remain hands-on from prototyping through implementation, integration, testing, deployment, troubleshooting, and continuous improvement, writing production code and working alongside engineers.
- Provide technical direction, architecture and code reviews, and mentoring to Agentic AI Engineers while partnering with their managers to align priorities and capacity.
- Define success measures, acceptance criteria, evaluation strategies, and production-readiness standards, including failure, adversarial, and regression testing.
- Partner with data architecture and governance teams to apply semantic models and domain context while aligning definitions, entities, relationships, and rules with enterprise standards.
- Embed security, responsible AI, governance, and regulatory controls into solutions, including access controls, guardrails, action limits, monitoring, and auditability.
- Coordinate rollout, business readiness, operational support, and continuous improvement while developing reusable patterns that accelerate future AI delivery.
- Help shape the AI tools roadmap by evaluating capabilities such as Snowflake Cortex, Microsoft Copilot, models, platforms, and vendor solutions, and recommending build-versus-buy approaches.
What You'll Bring- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related quantitative field with 12+ years of experience in software engineering, machine learning, or data engineering; or a Master's degree with 10+ years of relevant experience.
- 3+ years designing and leading production AI/ML solutions, including at least 2 years with LLM-based applications and experience deploying multiple agentic systems into production for real users.
- Strong hands-on Python and software engineering expertise, including designing, building, integrating, testing, deploying, and troubleshooting production AI solutions; API and tool integration; automated testing; error handling; version control; code review; and debugging non-deterministic systems.
- Deep practical knowledge of agentic architectures and patterns, including tool calling, multi-agent orchestration, MCP, A2A or similar agent interoperability approaches, RAG, context and state management, structured outputs, and human-in-the-loop workflows.
- Hands-on experience with an agent framework or SDK, with LangGraph strongly preferred, and major model APIs or managed AI platforms such as Amazon Bedrock.
- Strong architecture and production-engineering fundamentals, including API design, enterprise integration, authentication and authorization, security, cloud architecture and deployment, containers, CI/CD, observability, resilience, scalability, and cost trade-offs. AWS experience is preferred.
- Experience defining AI evaluation and production-readiness strategies, including test datasets, metrics, regression and adversarial testing, model-based evaluators, reliability, security, AI governance, and regulatory requirements.
- Proven ability to translate business processes into practical technical solutions and determine when agentic AI, enterprise tools, platform-native capabilities, low-code/no-code solutions, or custom development are appropriate based on business needs, readiness, governance, risk, and cost.
- Demonstrated ownership of complex, cross-functional solutions from discovery through production, including scope, milestones, dependencies, risks, adoption, and operational transition.
- Strong communication, stakeholder management, and executive-level presentation skills, with the ability to explain technical concepts clearly, navigate competing priorities, influence without direct authority, provide technical direction, and mentor engineers.
- Strategic and pragmatic business judgment, with a track record of turning business priorities into achievable technology solutions while balancing technical rigor, simplicity, speed, cost, risk, and operational needs.
Preferred Qualifications- Experience in property and casualty insurance, particularly underwriting or claims, or delivering AI solutions within another highly regulated industry.
- Experience with process mapping, workflow redesign, organizational adoption, and measurement of operational business outcomes.
- Hands-on experience with semantic layers, ontologies, knowledge graphs, or graph-based retrieval in production AI or analytics solutions.
- Experience with Snowflake Cortex or comparable analytics, AI, and business productivity platforms.
Salary Range: $140,000- $205,000
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/
