Lead AI Full Stack Software Engineer – Investments Technology

Role Summary

Lead the design, development, and deployment of production-grade agentic AI systems embedded directly into the investment lifecycle as part of the Investments Technology team. Work in cross-functional squads across Investments, Investments Technology, and TRP Labs to architect, build, and operationalize autonomous AI agents that interact with proprietary financial data, enterprise systems, and investment professionals.

Responsibilities

• Lead the design, development, and deployment of AI systems, providing technical mentorship and oversight to engineering squads.
• Collaborate in cross-functional squads to ensure responsible AI solutions are scaled across the enterprise.
• Partner with business stakeholders to develop agent-driven workflows that automate complex processes and generate actionable insights.
• Champion engineering excellence by establishing and enforcing best practices in AI development, continuous integration, and code quality.
• Oversee projects on agent orchestration, prompt engineering, and real-time, data-driven automation.
• Prioritize and manage technical debt, driving ongoing improvements in AI platforms and infrastructure.
• Proactively identify and pursue opportunities to apply AI agents for increased business value and operational efficiency.

Qualifications

• BS or MS in Computer Science or a related technical field (or equivalent experience), with 8+ years of progressive professional development experience in Python, including demonstrated leadership of engineering teams.
• Extensive hands-on expertise in architecting and delivering cloud-native solutions using AWS or Azure, containerized microservices, and agent frameworks.
• Hands-on experience building LLM-powered or agent-based systems in production.
• Exceptional analytical and problem-solving skills, with a proven ability to guide teams through complex technical challenges.
• Able to communicate with and influence both business stakeholders and technical teams.
• Demonstrated commitment to engineering excellence through setting and upholding standards for automated testing, code reviews, and continuous delivery.

Preferred Experience

• With retrieval-augmented generation (RAG), vector databases, MCP servers, agent orchestration frameworks, prompt evaluation and iteration, model benchmarking, and performance testing.
• Experience with Amazon Bedrock AgentCore, AWS Kiro, and OpenAI Codex.
• Experience in asset management, financial markets, or quantitative research environments.
• Solid understanding of financial markets, financial instruments, and financial datasets.