Build and productionize AI-powered solutions embedded with investment, corporate, and engineering teams. Responsibilities include gathering requirements, designing agentic workflows, developing AI agents and prompts, handling documents and market data, delivering solutions through production and user acceptance testing, and iterating based on stakeholder feedback. The role also contributes reusable AI patterns, engineering best practices, and product roadmap decisions.

Own the reliability, observability, incident response, and scalability of an internal agentic AI platform. Define SLOs, error budgets, and runbooks; support users; investigate root causes; monitor dependencies; and improve platform engineering practices. Build automation and applications using Python, REST APIs, databases, AWS, Kubernetes, and CI/CD pipelines. Collaborate with stakeholders to ensure AI agents, gateways, LLM proxies, and RAG pipelines deliver high availability, accuracy, and cost efficiency.

Perform T+1 reconciliations and resolve breaks across financial instruments, internal systems, prime brokers, and fund administrators. Collaborate with stakeholders to resolve operational issues, automate processes, implement strategic initiatives, standardize procedures, and improve workflows. The role also requires financial product expertise, advanced Excel and programming skills, large-data analysis, and experience with hedge fund operations and reconciliation migrations.