Join a pioneering force in the energy and commodities sector that is making substantial investments in applied AI. We are looking for a
Lead Machine Learning Engineer who will spearhead hands-on initiatives in building and deploying impactful large-scale models. This position provides a unique opportunity to shape the integration of machine learning within a billion-dollar trading operation, all in a collaborative, flat, and engineering-driven atmosphere.
Your Responsibilities - Design and implement machine learning systems from the ground up, incorporating GenAI and LLM-based applications.
- Collaborate closely with technical leads and business stakeholders to define use cases and prioritize model development.
- Take ownership of projects throughout their entire lifecycle: from data ingestion and feature engineering to experimentation and production deployment.
- Convert complex requirements into efficient, scalable, and modular Python code.
- Integrate models into internal tools and platforms to enhance decision-making processes for commercial and trading teams.
- Work with global teams (Europe & U.S.) on model design, tooling standards, and cross-region projects.
What We're Seeking - 5-7+ years of experience in developing and deploying ML models in production.
- Profound knowledge of Python and ML frameworks (e.g., PyTorch, TensorFlow, Transformers).
- Proven track record of applying LLMs or GenAI to solve real-world business challenges.
- Familiarity with modern cloud platforms (e.g., AWS) and containerization tools (e.g., Docker).
- Strong communication skills with the ability to collaborate across technical and non-technical teams.
- A product-oriented mindset with a knack for building, iterating, and refining based on user feedback.
- Experience in high-scale or data-intensive industries such as finance, trading, or logistics is preferred.
- A Master's or PhD in Computer Science, Machine Learning, Data Science, or a related field is preferred.