About
Flow Traders creates the conditions for high-performing people to do their best work and operates offices worldwide.
Responsibilities
•Design and implement AI agents that plan, reason, and execute multi-step workflows
•Build single-agent and multi-agent architectures for enterprise use cases
•Develop reusable agent patterns and templates for other teams
•Deploy agents on managed platforms, including Anthropic, Azure OpenAI, and GCP Vertex AI
•Configure secure execution environments, lifecycle management, and session handling
•Design human-in-the-loop escalations and approval gates for high-value actions
•Own the agent release lifecycle, including prompt and configuration versioning, staged rollouts, and canary releases
•Integrate agents with internal data platforms, APIs, and services
•Implement RAG pipelines, vector databases, and structured knowledge layers
•Build evaluation frameworks for accuracy, task completion, and reliability
•Monitor agent behavior and performance in production
•Implement fallback mechanisms, error handling, cost controls, and usage guardrails
•Ensure agents comply with security policies, data access constraints, and regulatory frameworks
•Maintain audit trails and explainability of agent actions
•Deliver solutions for investigation workflows, reporting, reconciliation, and decision support
•Implement safeguards against hallucinations, unsafe actions, and data leakage
•Translate workflows from Trading, Risk, Compliance, Finance, and Operations into agent-driven automation
Requirements
•Strong programming skills in Python
•Hands-on experience with LLM-based applications and agent systems
•Familiarity with agent frameworks such as LangChain, LangGraph, AutoGen, or CrewAI
•Experience with API development, microservices, and tool orchestration
•Knowledge of vector databases, RAG pipelines, and prompt engineering
•Experience with AWS, Azure, or GCP
•Experience with Docker and basic Kubernetes orchestration
•Experience with CI/CD pipelines, production deployment, and observability tools
•Ability to design goal-driven AI systems rather than static models
•Experience building multi-step reasoning workflows
•Ability to implement evaluation pipelines and production guardrails
•Awareness of cost, latency, and performance trade-offs in LLM systems
Conditions
•Competitive salary
•Annual discretionary profit share and comprehensive benefits
•Flow Academy and opportunities to attend domain-related conferences
•Daily catered lunch, coffee, a stocked kitchen, and snack bar
•In-house bar and lounge with arcade games, table tennis, billiards, and game consoles
•Company boat
•In-house gym and sessions with a nutritionist or personal trainer
•Bi-weekly massages
•Annual company trip and events throughout the year, including the New Year's Event
•Global rotations across offices worldwide
