Machine Learning Engineer (AI Agent Platform)

Description du poste

Arta is building the AI infrastructure for the next generation of wealth management We partner with leading financial institutions to power strategic initiatives that create real competitive advantage, particularly in making high-quality, personalised advice scalable Our platform enables intelligent agents to operate across core advisory workflows, from client servicing and suitability to portfolio research and analysis. These systems run in live, regulated environments and are embedded into how institutions serve their clients day to day You will design and build production-ready agent systems that sit at the core of how financial decisions are supported and delivered Build the AI Agent Platform Design and implement agent architectures (tool use, planning, memory, orchestration) Build systems for LLM orchestration, prompt management, and workflow execution Develop evaluation frameworks for agent quality, reliability, and safety Create benchmarking pipelines to measure model and system performance over time Enable Enterprise Deployment Build infrastructure for self-hosted and multi-tenant deployments Design systems that operate under enterprise constraints (security, latency, cost) Develop APIs and platform abstractions for external partners Bridge Research → Production Translate rapidly evolving LLM capabilities into stable, production-ready systems Partner with ML and product teams to integrate agents into real financial workflows Improve reliability, observability, and failure handling of agent systems What Makes This Role Different: This is not a research or prototype-focused role You will be responsible for shipping systems that operate in live financial environments. Your work directly supports institutional clients and real end users at some of the largest and fastest-growing financial institutions, not internal demos If you’re motivated by making agent systems work reliably at scale, in complex and regulated settings, this role will be a

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