Our client is a fast-growing commodities trading house headquartered in Switzerland with offices across Europe, Latin America, and Asia. Combining industry expertise with advanced technology, the company is modernizing global trade of base metals through digital and AI-driven platforms that enhance transparency, speed, and efficiency. Backed by top-tier investors, it is building next-generation solutions for quantitative research, trading signals, and intelligent decision-making.
As part of this, the team is building next-generation digital and AI-driven platforms to support quantitative research, trading signals, and intelligent decision-making. Backed by top-tier investors, they are shaping the future of raw materials trading and building a sustainable infrastructure for the global metals supply chain.
The open position is for an AI Engineer within the Quantitative Research Platform team. The role requires strong experience in Python, AWS, and modern AI/ML techniques, particularly in agentic AI systems and prompt engineering. The engineer will work closely with quants, researchers, and traders to design and scale AI-powered infrastructure for trading models, risk analysis, and automated decision-making.

REQUIREMENTS

• 3–5 years of experience in AI/ML engineering or applied data science;
• Strong proficiency in Python (NumPy, Pandas, PyTorch/TensorFlow, LangChain or similar);
• Familiarity with MCP (Model Context Protocol) or similar orchestration frameworks;
• Experience with AWS (EC2, S3, Lambda, SageMaker, Step Functions, etc.);
• Hands-on expertise in agentic AI frameworks, multi-agent systems, or LLM orchestration;
• Proficiency in Prompt Engineering for LLMs and workflow optimization;
• Understanding of financial markets, quantitative research, or commodities trading (preferably base metals);
• Strong software engineering background, including version control (Git), testing, and CI/CD;
• At least an Intermediate English level (both spoken and written).

• Design, build, and maintain AI agents to support trading, risk, and research workflows;
• Implement LLM-driven prompt engineering for data extraction, transformation, and knowledge integration;
• Collaborate with Quant Researchers to translate trading strategies into AI-enabled systems;
• Deploy and scale solutions on AWS cloud infrastructure with best practices in performance and security;
• Develop pipelines for integrating market, fundamental, and alternative datasets into AI/ML workflows;
• Partner with data engineers and software developers to integrate AI models into the Quantitative Research Platform;
• Contribute to the development of multi-agent coordination frameworks to support automated decision-making in commodity trading.