Join our Digital Engineering Studio and redefine the future of insurance through innovative ideas, data, and intelligent automation. This dynamic environment is designed for forward-thinking technologists eager to learn, build, and create at scale. You will work on critical projects spanning project governance, UI/UX engineering, API development, and modern web/mobile platforms, developing intuitive portals for self-service insurance purchases, policy renewals, and claims processing.

This role offers a unique opportunity for aspiring Data Engineers and AI enthusiasts. You will architect and implement data pipelines that power Retrieval-Augmented Generation (RAG) solutions, enhance intelligent search capabilities, and support AI-driven decision-making across our digital insurance platforms. Leverage AI-powered coding assistants and agentic development tools to accelerate your work and master best-in-class engineering practices. From integrating core policy administration systems to connecting with crucial government ecosystems, gain hands-on experience with large-scale, mission-critical systems.

We seek an Agentic AI Lead with strong practical and strategic expertise in architecting and delivering enterprise-scale intelligent, autonomous, and AI-enabled applications. This involves utilizing modern agentic and GenAI frameworks, cloud-native platforms, and scalable backend architectures. The role demands extensive experience in designing and developing robust backend and platform services, integrating enterprise APIs and diverse data sources, working with relational databases like PostgreSQL, and deploying production systems on Google Cloud Platform (GCP) with mature CI/CD pipelines.

Provide enterprise-wide technical and strategic leadership for the vision, architecture, and execution of large-scale agentic AI platforms and solutions across multiple business lines. Own the agentic AI technology roadmap, guiding the long-term evolution of agent architectures, platforms, and operating models. Lead the design and governance of complex, distributed agentic systems, including multi-agent ecosystems and autonomous workflows. Set architectural guardrails and engineering standards for agent design, orchestration, and failure recovery at an enterprise scale. Architect highly composable AI systems that integrate LLMs, ML models, enterprise tools, APIs, and cloud-native services into intelligent workflows. Drive large-scale enterprise integration, enabling seamless interaction between agentic platforms and legacy systems. Define and institutionalize enterprise reference architectures, design patterns, and engineering playbooks for agentic AI adoption. Champion Responsible AI at an enterprise level, embedding governance, explainability, and risk controls. Partner with executive stakeholders to shape AI-driven business transformation. Oversee system performance and economics, driving optimization across quality, accuracy, latency, and cost. Provide executive oversight for production operations, including incident management and platform resilience. Build and lead high-performing agentic engineering teams, mentoring senior technologists.

14–18 years of overall experience in advanced software engineering and AI systems, including leading enterprise-scale AI, GenAI, or autonomous agent initiatives. Proven track record in architecting and scaling agentic AI platforms across multiple domains and geographies. Deep expertise in multi-agent systems, autonomous decision-making, planning and reasoning architectures, and enterprise workflow automation. Advanced hands-on and conceptual mastery of LLMs, prompt strategies, reasoning frameworks, and evaluation methodologies. Strong experience with agent frameworks and platforms (e.g., LangChain, LangGraph, AutoGen, CrewAI) with the ability to design custom enterprise-grade agent foundations. Expert-level understanding of agent orchestration, including planning, tool invocation strategies, and exception handling. Deep knowledge of memory, state, and context management, including vector databases and knowledge graphs. Advanced experience with RAG systems, search relevance optimization, and hallucination mitigation. Demonstrated algorithmic and model stewardship, including evaluation frameworks and bias analysis. Extensive experience deploying and operating AI platforms on Google Cloud Platform (GCP) using cloud-native architectures. Experience working with PostgreSQL databases. Strong leadership in MLOps and AI Platform Engineering, including CI/CD and testing at scale. Expertise in AI observability and governance, including telemetry, tracing, and cost governance. Strong command of AI security, privacy, and regulatory compliance. Preferred: Deep exposure to General Insurance or other regulated industries.