AI Agent Developer

AI Agent Developer

Job Overview Lead the development of autonomous AI agents that revolutionize investment workflows and client interactions. You'll build sophisticated multi-agent systems capable of independent research, portfolio optimization, and real-time market analysis, positioning our firm at the forefront of agentic AI in finance.

Key Responsibilities

•Design and implement autonomous AI agents for investment research, risk assessment, and portfolio management 

•Build multi-agent architectures with specialized roles (research agents, trading agents, compliance agents)

•Develop agent communication protocols and collaborative workflows for complex financial tasks

•Implement reinforcement learning systems for adaptive agent behavior in dynamic markets

•Create agent orchestration frameworks managing task delegation, priority queuing, and resource allocation

•Integrate agents with existing trading systems, risk management platforms, and client advisory tools

•Collaborate with quantitative researchers to embed domain knowledge into agent decision-making processes

Required Qualifications

•Master's degree in Computer Science, Artificial Intelligence, or related field

•Experience developing AI/ML systems with focus on autonomous agents or multi-agent systems

•Strong background in reinforcement learning, decision theory, and agent-based modeling

•Experience with LLM-powered agentic frameworks (LangGraph, AutoGen, CrewAI) 

•Proficiency in Python with deep learning frameworks (PyTorch, TensorFlow)

•Knowledge of distributed systems, message queuing, and microservices architecture

•Understanding of financial markets, trading workflows, or investment management processes

Preferred Qualifications

•PhD in Computer Science, AI, or quantitative field with focus on multi-agent systems

•Previous experience in financial services, particularly algorithmic trading or quantitative research 

•Publications or research in autonomous agents, multi-agent coordination, or financial AI

•Experience with alternative data integration and real-time processing systems

Technical Skills

•Core AI: PyTorch, TensorFlow, reinforcement learning libraries (Stable Baselines3, RLLib) 

•Agent Frameworks:  LangGraph, AutoGen, multi-agent communication protocols

•Programming: Python, C++, distributed computing, async programming

•Infrastructure: Docker, Kubernetes, message queues (Redis, RabbitMQ), microservices

•Data Processing: Real-time streaming (Kafka, Pulsar), time series databases

•Financial Tools: Trading APIs, market data feeds, portfolio optimization libraries

•Monitoring: Agent performance tracking, decision audit trails, governance frameworks