Senior Machine Learning Engineer

Machine Learning Engineer

Location: Houston, TX

Working Pattern: 5 days per week onsite

Experience: 5+ years

Company Description

Our client is a large global energy and commodities business operating across international markets.

Technology, data science and machine learning play an increasingly important role across the organisation, and the business is continuing to invest in ML and Generative AI capabilities.

Due to continued growth, they are looking for an experienced Machine Learning Engineer to join their Houston-based Data Science and Machine Learning team.

The Role

This is a hands-on position with exposure across the full machine learning lifecycle.

You will work closely with data scientists, ML specialists, software engineers and commercial teams to identify problems, develop solutions and deploy production-grade machine learning applications.

Projects span machine learning, time-series forecasting, NLP and Generative AI, with the opportunity to work on commercially important problems involving pricing, supply and demand, operational optimisation and other business-critical applications.

You will also play an important role in the continued development and adoption of the organisation's internal Generative AI platform.

Responsibilities

• Design, develop and deploy end-to-end machine learning and data science solutions.
• Build production ML systems from data ingestion and feature engineering through model deployment and monitoring.
• Develop and extend internal GenAI and LLM applications, including integrations, data connectors and prompt engineering.
• Apply techniques including time-series forecasting, NLP, classification and Generative AI to real-world commercial problems.
• Build robust, well-tested production-quality Python code.
• Contribute to ML pipelines, data orchestration and model-serving infrastructure.
• Integrate ML outputs into existing applications, dashboards and business workflows.
• Work directly with commercial and operational stakeholders to identify valuable ML opportunities.
• Communicate model results, assumptions and limitations clearly to non-technical stakeholders.
• Participate in code reviews, experimentation and technical decision-making.

What We're Looking For

• 5+ years' industry experience developing and deploying machine learning or statistical models.
• Strong Python skills for both data science and software engineering.
• Experience delivering end-to-end ML solutions into production.
• Strong understanding of supervised and unsupervised learning.
• Experience with time-series modelling.
• Experience with NLP, LLMs and Generative AI applications.
• Experience with frameworks such as PyTorch, scikit-learn and Transformers.
• Cloud experience, preferably AWS.
• Experience with modern MLOps practices including Docker/Kubernetes, CI/CD, model deployment and monitoring.
• Experience with data orchestration tools such as Airflow or Dagster.
• Strong analytical and problem-solving ability.
• Comfortable working directly with both technical and non-technical stakeholders.

A Master's degree or equivalent in Computer Science, Statistics, Mathematics, Data Science or another quantitative discipline is preferred.

Desirable

Experience within energy, commodities trading or financial markets would be beneficial but is not essential.

Additional experience with any of the following would also be valuable:

• Financial or trading-related time-series modelling
• Econometric approaches such as ARIMA or cointegration
• Dash, Streamlit or similar interactive applications
• Cloud-based ETL/ELT pipelines

The Opportunity

This is an opportunity to join an experienced ML and Data Science team within a large international organisation where machine learning is being applied to complex, commercially important problems.

You will have significant ownership over your work, direct exposure to business stakeholders and the opportunity to help shape how ML and Generative AI are adopted across the organisation.

Location: Houston, TX

Working Pattern: 5 days per week in the office