Mexico City, MX
Actively hiring, first seen by us 4 days agoEmployment type in the employer’s posting: Full timeFrom the employer’s posting
The role: Senior Machine Learning Engineer
Location: Mexico City, Paseo de la reforma 180, 2 days per week onsite.
Design and productionize scalable machine learning and AI solutions
Build data and ML pipelines from preparation through monitoring
Write reusable Python software and services for production applications
Apply MLOps practices such as CI/CD, versioning and observability
Mentor the team and contribute to code reviews and architecture decisions
The role: Senior Machine Learning Engineer
Location: Mexico City, Paseo de la reforma 180, 2 days per week onsite.
Design, develop and productionize scalable machine learning and AI solutions
Build robust data and ML pipelines that support the complete lifecycle
Develop high-quality, reusable Python software and services
Establish and apply strong MLOps and software engineering practices
Contribute to engineering standards, architecture decisions, code reviews and technical mentoring across the team
Demonstrated experience designing and implementing production-grade machine learning or advanced analytical systems.
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.
From the employer’s posting.
We're more than 35,000 strong worldwide—so we're able to understand nuances while having a broad perspective.
In the employer’s words · Read in context
Actively hiringFirst seen by us 4 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 4
Early. Similar jobs usually come down between day 8 and day 76.
At S&P Global, jobs usually come down by about day 18.
From 5,517 similar postings that came down, and 136 of S&P Global’s. Coming down is not always a hire.
28 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
• 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.
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 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.
• 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.
• 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 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.
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New ML Engineer postings at the employers we follow are up 10% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Grade Level (for internal use):
10S&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 Global 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
At S&P Global Energy, our comprehensive view of global energy and commodities markets enables our customers to make superior decisions and create long-term, sustainable value. Our four core capabilities are: Platts for news and pricing; CERA for research and advisory; Horizons for energy expansion and sustainability solutions; and Events for industry collaboration.
S&P Global Energy is a division of S&P Global (NYSE: SPGI). S&P Global enables businesses, governments, and individuals with trusted data, expertise, and technology to make decisions with conviction. We are Advancing Essential Intelligence through world-leading benchmarks, data, and insights that customers need in order to plan confidently, act decisively, and thrive economically in a rapidly changing global landscape. Learn more at www.spglobal.com/energy.
What’s In It For You?
Our Mission:
Advancing Essential Intelligence.
Our People:
We're more than 35,000 strong worldwide—so we're able to understand nuances while having a broad perspective. Our team is driven by curiosity and a shared belief that Essential Intelligence can help build a more prosperous future for us all.From finding new ways to measure sustainability to analyzing energy transition across the supply chain to building workflow solutions that make it easy to tap into insight and apply it. We are changing the way people see things and empowering them to make an impact on the world we live in. We’re committed to a more equitable future and to helping our customers find new, sustainable ways of doing business. Join us and help create the critical insights that truly make a difference.
Our Values:
Integrity, Discovery, Partnership
Throughout our history, the world's leading organizations have relied on us for the Essential Intelligence they need to make confident decisions about the road ahead. We start with a foundation of integrity in all we do, bring a spirit of discovery to our work, and collaborate in close partnership with each other and our customers to achieve shared goals.
Benefits:
We take care of you, so you can take care of business. We care about our people. That’s why we provide everything you—and your career—need to thrive at S&P Global.
Our benefits include:
Health & Wellness: Health care coverage designed for the mind and body.
Flexible Downtime: Generous time off helps keep you energized for your time on.
Continuous Learning: Access a wealth of resources to grow your career and learn valuable new skills.
Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs.
Family Friendly Perks: It’s not just about you. S&P Global has perks for your partners and little ones, too, with some best-in class benefits for families.
Beyond the Basics: From retail discounts to referral incentive awards—small perks can make a big difference.
For more information on benefits by country visit: https://spgbenefits.com/benefit-summaries
Global Hiring and Opportunity at S&P Global:
At S&P Global, we are committed to fostering a connected and engaged workplace where all individuals have access to opportunities based on their skills, experience, and contributions. Our hiring practices emphasize fairness, transparency, and merit, ensuring that we attract and retain top talent. By valuing different perspectives and promoting a culture of respect and collaboration, we drive innovation and power global markets.
Recruitment Fraud Alert:
If you receive an email from a spglobalind.com domain or any other regionally based domains, it is a scam and should be reported to reportfraud@spglobal.com. S&P Global never requires any candidate to pay money for job applications, interviews, offer letters, “pre-employment training” or for equipment/delivery of equipment. Stay informed and protect yourself from recruitment fraud by reviewing our guidelines, fraudulent domains, and how to report suspicious activity here.
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Equal Opportunity Employer
S&P Global is an equal opportunity employer and all qualified candidates will receive consideration for employment without regard to race/ethnicity, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, military veteran status, unemployment status, or any other status protected by law. Only electronic job submissions will be considered for employment.
If you need an accommodation during the application process due to a disability, please send an email to: EEO.Compliance@spglobal.com and your request will be forwarded to the appropriate person.
US Candidates Only: Know Your Rights: Workplace discrimination is illegal
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Mexico City, MX
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