Senior Machine Learning Engineer - Agentic Workflow

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. Revenues in 2024 were $331bn.

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 seeking a Senior Machine Learning Engineer / Platform Engineer to design and build a production-grade agentic workflow platform. This role sits at the intersection of LLM systems engineering, distributed platforms, and applied ML, with a strong emphasis on orchestration, reliability, and extensibility. You will be responsible for architecting and implementing agent-based workflows that integrate large language models, retrieval systems, structured knowledge, and external APIs—designed for robustness, observability, and real-world business use.

- Design and implement multi-agent and single-agent workflows using orchestration patterns and tools, context engineering, memory management, and guardrail strategies.

- Design RAG pipelines incorporating vector search, hybrid retrieval, and citation tracking.

- Implement knowledge graph–backed reasoning, including ontologies, entity resolution and graph-based context construction.

- Design evaluation frameworks for agent task completion correctness, quality, cost, and latency.

- Develop and deploy machine learning models, focusing on production readiness, scalability, and performance.

- Collaborate with data scientists to transition experimental models into robust, production-grade applications.

- Integrate with collaboration platforms (e.g., Teams, alerting systems) for intelligent distribution of insights.

- Implement and manage CI/CD pipelines to automate deployment, testing, and monitoring of models.

- Architect and deploy systems on AWS, leveraging compute, storage and security services

Qualifications

- Bachelor’s or master’s degree in computer science, Engineering, or related field.

- 6+ years of experience in software engineering, ML engineering, or platform engineering.

- Strong proficiency in writing production-grade Python, and experience with Claude Code or Cursor.

- Hands-on experience with LLM-based systems, including:

- LangChain / LangGraph

- MCP

- Langsmith

- Claude or comparable frontier models

- AWS AgentCore or comparable agentic frameworks

- Solid understanding of RAG architectures, embeddings, and vector search.

- Experience designing and consuming APIs (REST and/or async/event-driven).

- Strong cloud engineering experience on AWS.

- Knowledge of how to fine-tune frontier models to specific domain knowledge

- Experience with distillation, quantization and small language models is a plus

- Experience deploying traditional machine learning models into production environments using MLOps tools and best practices.

- Knowledge of distributed systems, large-scale model optimization, and API development.

- Exceptional ability to work on a team – especially a dynamic, innovative “tiger team” developing early stage PoC systems.

- Strong understanding of container orchestration and cloud-native application design.

- Ability to work in dynamic environments, handling rapid experimentation and iterative development.

Additional Information

Personal Characteristics

- A self-motivated individual who thrives on seeing the results of their work and its impact on the business

- Strong communication skills, both verbally and in writing

- A keen sense for the art of the possible

- Proven ability to be flexible and work hard, both independently and collaboratively

- Methodical and organized - in general, in experimental design, and in code!

- Attention to detail with strong analytical, mathematical, and problem-solving skills

- An interest in learning about the energy commodities space

- Resourceful and able to think creatively and adapt in a dynamic and energetic environment

- Team player, with an open, non-political style and a high level of personal integrity

- Desire to be a thought-partner in a fast-growing team, and make an impact at a business that sits at the heart of the world’s energy flows

This Role is located in Houston, TX - In office 5x a week

All your information will be kept confidential according to EEO guidelines.