ai engineer

Flow Traders is a financial trading firm developing enterprise-grade AI agents and automation workflows for internal business processes across Trading, Risk, Compliance, Finance, and Operations.

• Design and implement AI agents capable of planning, reasoning, and executing 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 operate within 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.

• 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 cloud platforms such as AWS, Azure, or GCP;
• Experience with Docker and basic Kubernetes orchestration;
• Experience with CI/CD pipelines, production deployment, and observability tooling;
• 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.

• Competitive salary;
• Annual discretionary profit share;
• Comprehensive benefits;
• Flow Academy and opportunities to attend domain-related conferences;
• Daily catered lunch, coffee, kitchen, and snack bar;
• In-house bar and lounge with arcade, 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;
• Global rotations across offices worldwide.