Portfolio Manager, Agentic Systems

Portfolio Manager

Agentic Systems Utilization

Reinforcement Learning Tuning

Model Development

Custom Agentic Development

Human-in-the-Loop Oversight

Responsibilities

• Take risk, manage P&L, and make trading decisions within defined risk parameters while developing expertise in quantitative portfolio management principles
• Deploy and work with cognitive reasoning systems for quantitative modeling problems, leveraging planning, tool use, memory, reflection, and collaboration capabilities
• Utilize and interact with agentic systems including planning algorithms, memory architectures, reflection mechanisms, and collaborative reasoning patterns that support autonomous decision-making in quantitative trading environments
• Adjust hyperparameters of reinforcement learning training processes to optimize system performance
• Contribute to deep learning model development for the PM model layer and custom agentic workflows
• Build and customize agentic workflows and tools tailored to portfolio management needs and specific trading strategies
• Execute human-in-the-loop decisions and checks ensuring that traded strategies meet quant trading acceptance criteria

Requirements

• Advanced degree in a quantitative field (Computer Science, Mathematics, Physics, Statistics, Engineering, or related discipline)
• Minimum of 10 years of experience, PM experience is not required but preferred
• Familiarity with financial markets
• Experience with python-based deep learning model development
• Willingness to learn portfolio management discipline, including P&L responsibility and risk management
• Hands-on experience with agentic AI frameworks
• Deep knowledge of the core capabilities of agentic systems: planning, tool use, memory, reflection, and collaboration
• Experience applying reinforcement learning methodologies to develop autonomous systems that learn and improve through policy optimization, reward modeling, and outcome-based feedback loops
• Ability to adjust hyperparameters and tune training processes for reinforcement learning systems

Qualifications

• Excellent degree in a quantitative field
• Minimum of 10 years of relevant experience
• Experience with Python-based deep learning model development
• Strong understanding of financial markets
• Knowledgeable in portfolio management principles
• Experience with agentic AI frameworks
• Expertise in reinforcement learning methodologies
• Ability to adjust hyperparameters and tune training processes for reinforcement learning systems

Skills

• Advanced degree in a quantitative field
• Experience with Python-based deep learning model development
• Understanding of financial markets
• Portfolio management principles
• Agentic AI frameworks
• Reinforcement learning methodologies
• Ability to adjust hyperparameters and tune training processes for reinforcement learning systems

Benefits

• Total compensation organization
• Base salary
• Discretionary performance bonus
• Broad range of asset classes and global markets
• Opportunity to shape the future of quantitative finance
• Collaborative team environment
• Competitive total compensation package

Pay

Base Pay Range: 150,000 USD

Schedule

Full-time