About the Role:

Grade Level (for internal use):

10

S&P; Global Commodity Insights

The role: Senior Machine Learning Engineer

Location: Mexico City, Paseo de la reforma 180, 2 days per week onsite.

About the Role

S&P; General Commodity Insights is looking for an experienced Machine Learning Engineer to join our Data Science & Modelling team.

In this role, you will design, build and productionize machine learning and AI solutions using S&P; Global's extensive data and domain expertise. You will work at the intersection of machine learning, software engineering, data engineering and domain modelling, partnering with data scientists, modelers, engineers, product teams and subject‑matter experts.

Your work will support a range of business outcomes, including commercial products, advanced forecasting and modelling capabilities, intelligent automation, internal decision support and client‑facing solutions.

Responsibilities:

- Design, develop and productionize scalable machine learning and AI solutions for complex business and modelling problems.
- Build robust data and ML pipelines that support the complete lifecycle from data preparation and feature engineering through training, deployment, evaluation and monitoring.
- Develop high‑quality, reusable Python software and services that integrate ML capabilities into production applications and analytical platforms.
- Work with large and complex structured and unstructured datasets to improve the quality, accuracy and intelligence of our products and modelling capabilities.
- Apply modern ML techniques, and where appropriate Generative AI, LLMs and agent‑based approaches, to solve business and domain problems.
- Establish and apply strong MLOps and software engineering practices,



including automated testing, CI/CD, versioning, observability and reproducibility.
- Build solutions using modern cloud and distributed‑computing platforms.
- Evaluate model quality and system performance using appropriate quantitative metrics, validation frameworks and monitoring.
- Collaborate closely with data scientists, modelers, data engineers, product teams and energy‑domain experts to translate business and modelling requirements into robust technical solutions.
- Investigate emerging ML and AI technologies and identify practical opportunities to apply them within Data Science & Modelling.
- Contribute to engineering standards, architecture decisions, code reviews and technical mentoring across the team.

Requirements:

- 7+ years of professional software development or engineering experience, with strong proficiency in Python.
- Demonstrated experience designing and implementing production‑grade machine learning or advanced analytical systems.
- Strong knowledge of software engineering fundamentals, including object‑oriented design, modular architecture, testing, clean‑code practices and design patterns.
- Strong experience working with SQL and large‑scale data platforms.
- Experience building scalable data processing and ML pipelines using technologies such as Apache Spark / PySpark and Databricks or comparable platforms.




- Experience deploying and operating solutions in a cloud environment such as AWS.
- Understanding of the ML lifecycle, including training, validation, deployment, monitoring and model/version management.
- Experience with modern engineering and DevOps practices including Git, automated testing, CI/CD and containerization.
- Ability to design reliable, performant and maintainable systems handling large or complex datasets.
- Strong analytical and problem‑solving skills.
- Ability to communicate effectively with both technical and non‑technical stakeholders and work successfully in cross‑functional Agile teams.
- Bachelor's or advanced degree in Computer Science, Engineering, Mathematics, Data Science or a related quantitative discipline, or equivalent practical experience.

Nice to Have

- Experience working with energy, oil & gas, commodities or financial/market data.
- Experience with Databricks and modern lakehouse architectures.
- Experience building or integrating Generative AI, LLM, RAG or agentic AI solutions.
- Experience with ML lifecycle and orchestration technologies such as MLflow, Airflow or equivalent platforms.
- Experience with model evaluation, observability and production monitoring.
- Experience developing APIs or services for integrating ML capabilities into production applications.
- Familiarity with deep‑learning frameworks such as PyTorch or TensorFlow.
- Experience working with time‑series forecasting, optimization, simulation or other advanced modelling techniques.

NOTE: As part of the process a final in person evaluation is a mandatory requirement.

About S&P; Global Energy

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📌 Senior ML Engineer (Chihuahua)
🏢 S&P Global
📍 Chihuahua