A global investment-management organization is expanding an AI technology team responsible for building the shared infrastructure behind enterprise AI applications. The team is moving beyond isolated experimentation and creating a scalable platform that enables engineers and business teams to develop, deploy, evaluate, and operate AI agents in production. This engineer will own meaningful pieces of that platform end-to-end. The work spans agent frameworks, RAG infrastructure, MCP services, developer tooling, enterprise data integration, observability, evaluation, authentication, and access control. This is a hands-on engineering position for someone who wants to build the infrastructure behind enterprise agentic AI rather than focus only on individual prototypes. RESPONSIBILITIES Own components of an enterprise AI platform from evaluation and architecture through production deployment.Build frameworks and tooling supporting AI agents and agentic applications.Develop RAG and data-access patterns connecting internal information to AI applications.Build and operate MCP servers, gateways, plugins, and related integration services.Create evaluation and observability capabilities for testing and monitoring AI systems.Build secure authentication, authorization, and entitlement patterns around AI capabilities.Turn recurring application patterns into reusable platform components.Improve engineering standards, reliability, documentation, and developer workflows. Role Requirements Strong software-engineering background with significant hands-on development experience.Strong Python development skills.Experience building AI-enabled applications, agentic workflows, or LLM infrastructure.Hands-on experience with RAG or enterprise-retrieval architectures.Understanding of how proprietary datasets are securely exposed to AI applications.Experience designing production-quality services with appropriate testing and monitoring.Ability to evaluate technical alternatives and make pragmatic architecture decisions.Strong communication and ability to collaborate across engineering teams. PREFERRED EXPERIENCE AWS experience.MCP development experience.Experience building internal developer platforms or engineering tooling.AI evaluation or observability experience.DataDog or similar tooling.Identity, authentication, or entitlement experience.Financial-services or investment-management technology experience.