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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead AI Solutions Architect - Agentic AI (Hybrid Boston or Webster) - **Company:** MAPFRE - **Location:** Webster, MA, United States - **Experience:** Expert - **Salary:** $140,000.0 - $205,000.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Business Analytics Applications, ARM Architecture, User Authentication, Automation of Tests, Cloud Engineering, Code Review, Information Systems, Continuous Integration, Custom Software, Data Architecture, Information Engineering, Data Integration, Software Debugging, Graph Database, Python (Programming Language), Machine Learning, Regression Testing, Microsoft Copilot, Software Engineering, Systems Integration, Retrieval-Augmented Generation, Snowflake, Multi-Agent Systems, Generative AI, Agentic-AI, Data Layers, AI Platforms, Information Technology, Amazon Bedrock, Production Code, Low-code, Enterprise Integration, Api Design, Software Version Control, Agent2Agent Protocol - **Published:** October 15, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=1055e2424719c73d ## About the Role * 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., * 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. ## Description We'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.