AI Engineer

Overview

The open position is for an AI Engineer within the Quantitative Research Platform team at Agile Fuel | World-class Dedicated Engineering Teams . 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.

Responsibilities

• 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.

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).

Bonus points

• Background in quantitative finance or exposure to commodity trading;
• Experience with reinforcement learning, optimization, or simulation frameworks;
• Knowledge of data engineering workflows (ETL pipelines, data lakes, APIs);
• Previous work in high-frequency or systematic trading environments.

Benefits

• People-oriented management without bureaucracy;
• Flexible schedule;
• 25 working days of annual paid vacation;
• Paid sick leaves;
• Friendly and engaging professional team;
• Opportunities for self-realization, career, and professional growth;
• Accounting and legal support.

Seniority level

• Mid-Senior level

Employment type

• Full-time

Job function

• Engineering and Information Technology

Industries

• IT Services and IT Consulting