AI Engineer

Job Description

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.

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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.

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Responsibilities

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• Own components of an enterprise AI platform from evaluation and architecture through production deployment.
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• Build frameworks and tooling supporting AI agents and agentic applications.
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• Develop RAG and data-access patterns connecting internal information to AI applications.
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• Build and operate MCP servers, gateways, plugins, and related integration services.
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• Create evaluation and observability capabilities for testing and monitoring AI systems.
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• Build secure authentication, authorization, and entitlement patterns around AI capabilities.
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• Turn recurring application patterns into reusable platform components.
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• Improve engineering standards, reliability, documentation, and developer workflows.
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Role Requirements

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• Strong software-engineering background with significant hands-on development experience.
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• Strong Python development skills.
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• Experience building AI-enabled applications, agentic workflows, or LLM infrastructure.
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• Hands-on experience with RAG or enterprise-retrieval architectures.
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• Understanding of how proprietary datasets are securely exposed to AI applications.
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• Experience designing production-quality services with appropriate testing and monitoring.
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• Ability to evaluate technical alternatives and make pragmatic architecture decisions.
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• Strong communication and ability to collaborate across engineering teams.
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Nice to Have

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• AWS experience.
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• MCP development experience.
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• Experience building internal developer platforms or engineering tooling.
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• AI evaluation or observability experience.
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• DataDog or similar tooling.
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• Identity, authentication, or entitlement experience.
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• Financial-services or investment-management technology experience.
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