Forward Deployed Engineer

Company Description Vitol is a leader in energy and commodities. Vitol produces, manages and delivers energy and commodities to consumers and industry worldwide. In addition to its primary business of trading, Vitol is invested in infrastructure globally, with $10+billion invested in long-term assets. Vitol’s customers include national oil companies, multinationals, leading industrial companies and utilities. Founded in Rotterdam in 1966, today Vitol serves its customers from some 40 offices worldwide. Our people are our business. Talent is precious to us and we create an environment in which individuals can reach their full potential, unhindered by hierarchy. Our team comprises more than 65+ nationalities and we are committed to developing and sustaining a diverse work force. Learn more about us here. This Role is located in Houston, TX - In office 5x a week Job Description We are looking for a hands-on Forward Deployed AI Engineer to join our global AI team. This is a hybrid role across engineering and commercial teams. You will sit directly with commercial users, build and ship agentic solutions that solve real workflow problems, and communicate technical requirements back to our foundational engineering teams. You will own technical design and delivery from prototype to production. As Vitol's AI portfolio grows, you will help shape what gets built, in what order, and [... source excerpt omitted ...] tioners who are solving some of the most challenging and impactful problems the energy industry is facing, as well as pushing the boundaries around the 'art of the possible'. Key Responsibilities Embed with commercial teams to understand their workflows, constraints, and business objectives; identify high-value opportunities for AI solutions Translate business problems into clear technical requirements, solution designs, and delivery plans to build, integrate, and deploy agentic solutions Development of the firm's internal GenAI platform, extending its capabilities through new integrations, data connectors, MCP servers, tool-calling, and domain-specific skills Apply a broad range [... source excerpt omitted ...] ing LLMs, RAG, NLP, time-series forecasting, and optimization, to commodity pricing, supply/demand signals, trade flow analysis, and operational optimization problems Communicate requirements, technical constraints, and reusable patterns back to the foundational AI and platform engineering teams Present capabilities, assumptions, and limitations clearly to traders and senior management, supporting confident commercial decision-making and building trust in AI outputs among technical and non-technical audiences Champion AI best practice on the desk and across the team: participate in code reviews, experiment and evaluation design, and tooling decisions; mentor colleagues and help ra