Global Markets - Senior AI Platform Engineer
Listed on UBS’s official careers site. Listed as of October 20, 2026. Check that it is still open and apply there.
Apply on UBS’s careers site →Job description
We are seeking a hands-on Senior AI Platform Engineer to design and deliver scalable AI solutions across our engineering ecosystem. The successful candidate will work at the intersection of AI, platform engineering, developer productivity and operations, developing agentic frameworks, AI-first APIs, autonomous agents and enterprise knowledge systems. This role will help build the next generation of AI-enabled engineering capabilities across our algo trading platforms, including agentic systems, AI-first APIs, autonomous operational agents and governed knowledge platforms.
Key Responsibilities
- Design and implement a Unified Knowledge Layer for AI agents, including context management, indexing, retrieval, governance and distribution strategies.
- Evaluate and productionise approaches for context delivery, including MCP architectures, local retrieval models, indexed code repositories and distributed knowledge platforms.
- Establish governance processes for AI skills, prompts and reusable agent capabilities, including approval, versioning and deployment standards.
- Build and maintain a portfolio of AI-first APIs across platform services including our core platform components, KDB, Airflow, configuration services, observability tooling, storage platforms and alerting systems.
- Develop agentic applications, including conversational assistants and autonomous support agents capable of investigating operational issues, generating incident reports and proactively notifying stakeholders.
- Create reusable frameworks that enable engineering teams to deploy autonomous AI agents for use cases such as merge request reviews, remediation workflows and platform support.
- Build AI-enabled analytics capabilities over engineering and operational datasets, ensuring appropriate governance, entitlements and data controls.
- Define evaluation frameworks to measure AI solution accuracy, latency, cost, reliability and operational effectiveness across model and platform changes.
- Collaborate closely with platform engineers, quant-developers, DevOps and operational support teams to embed AI into engineering workflows.
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