Senior ML Engineer
S&P Global2 days ago
Mexico City, MexicoSenior
Responsibilities
- Design, develop, and productionize scalable machine learning and AI solutions for complex business and modeling problems.
- Build data and ML pipelines covering data preparation, feature engineering, training, deployment, evaluation, monitoring, and version management.
- Develop reusable Python software and services that integrate ML capabilities into production applications and analytical platforms.
- Work with large structured and unstructured datasets to improve product and modeling quality.
- Apply modern ML techniques, Generative AI, LLMs, and agent-based approaches where appropriate.
- Establish 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 quantitative metrics, validation frameworks, and monitoring.
- Translate business and modeling requirements into technical solutions with cross-functional and energy-domain teams.
- Investigate emerging ML and AI technologies and identify practical applications.
- Contribute to engineering standards, architecture decisions, code reviews, and technical mentoring.
Requirements
- 7+ years of professional software development or engineering experience with strong Python proficiency.
- Experience designing and implementing production-grade machine learning or advanced analytical systems.
- Strong knowledge of object-oriented design, modular architecture, testing, clean-code practices, and design patterns.
- Strong experience with SQL and large-scale data platforms.
- Experience building scalable data-processing and ML pipelines with Apache Spark or 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 Git, automated testing, CI/CD, and containerization.
- Ability to design reliable, performant, and maintainable systems for large or complex datasets.
- Strong analytical, problem-solving, communication, and cross-functional collaboration skills.
- Bachelor's or advanced degree in Computer Science, Engineering, Mathematics, Data Science, or a related quantitative discipline, or equivalent practical experience.
- Experience with energy, oil and gas, commodities, or financial/market data is preferred.
- Experience with Databricks and lakehouse architectures is preferred.
- Experience with Generative AI, LLM, RAG, or agentic AI solutions is preferred.
- Experience with ML lifecycle and orchestration technologies such as MLflow or Airflow is preferred.
- Experience with model evaluation, observability, production monitoring, APIs, or ML services is preferred.
- Familiarity with PyTorch or TensorFlow is preferred.
- Experience with time-series forecasting, optimization, simulation, or other advanced modeling techniques is preferred.
Benefits
- Health and wellness coverage.
- Generous flexible time off.
- Continuous learning resources and career development support.
- Retirement planning, continuing education, company-matched student loan contributions, and financial wellness programs.
- Family-friendly benefits and perks.
- Retail discounts and referral incentive awards.
- Hybrid work in Mexico City with two days per week onsite.
- A final in-person evaluation is mandatory.