AI Engineer | Energy & Commodities Trading
Join a small, experienced AI team at a global energy and commodities trading firm. This is a hands-on build role. You'll work directly with traders, operations, and support teams to design and deploy LLM-based automation that delivers measurable commercial value. Expect real ownership from day one: you'll drive your own deliverables with a high degree of autonomy.
What You'll Do
- Partner with end-users across trading, operations, and support to find automation opportunities and build LLM solutions around their workflows
- Design, prototype, and iterate on agentic AI workflows
- Build production-ready prompts, AI Skills, and MCPs using frontier models
- Translate business problems into technical requirements, designs, and delivery plans
- Gather user feedback, refine solutions, and document your work for the team
What You Bring
- Degree in Computer Science, Engineering, Mathematics, or a related field
- 3 to 5 years of experience in software, data, or AI engineering
- Strong Python skills with clean, modular, well-documented code
- Hands-on experience building with LLMs, including prompting and agentic patterns (tool use, function calling, or MCP)
- Working knowledge of Git, Docker, and modern full-stack practices
- Ability to work with non-technical users and quickly learn new domains
- Interest in commodities markets, energy flows, and trading
Nice to Have
- Experience building MCP servers or clients, RAG pipelines, or multi-agent systems
- LLM evaluation or benchmarking experience
- Cloud experience (AWS, Azure, or GCP)
- Exposure to quantitative finance or derivatives
- Strong Excel skills
- Shipped products or side projects
Why This Role
- Work across oil, gas, metals, and shipping markets at one of the world's largest trading firms
- Shape how AI is used for automation and decision support in a fast-paced trading environment
- Build with current agentic frameworks and frontier model access
GenAI Engineer in houston at Unknown Company
This position is listed as full time and onsite.