We need an English-fluent Senior Data Engineer / Machine Learning Engineer, based in Latin America, available to work remotely.

We are looking for someone with exceptional communication and relationship-building skills, who embraces changes while maintaining strong attention to detail. An interested and proactive person, who's constantly learning and improving their skills.

Are you the one we are looking for?

Responsibilities:

• Design and build scalable data ingestion pipelines for diverse sources: Grid and market data (e.g., telemetry, operational datasets, filings), Geospatial data (satellite imagery, maps, infrastructure layers), Weather and environmental data, Time-series load and generation data.
• Create clean, versioned, query-able datasets suitable for both ML training and analytics.
• Develop canonical data models / schemas representing grid topology and asset relationships.
• Ensure data quality, lineage, reproducibility, and observability across pipelines.
• Engineer temporal, spatial, and relational features across heterogeneous datasets.
• Build representations that capture: Network topology (connectivity, constraints, hierarchy), Time-dependent behavior (load, generation, congestion, weather), Physical constraints and operational limits.
• Collaborate with physics-based modeling efforts (e.g., power-flow abstractions) and integrate outputs into ML workflows.
• Train and deploy time-series forecasting models for: Load, Renewable generation (wind, solar), Grid conditions and system stress indicators, Work with multi-horizon forecasting (short-term operational + long-term planning).
• Implement models ranging from: Classical statistical methods (when appropriate), Modern ML approaches (deep learning, sequence models, hybrid physics-MLmodels)
• Evaluate models rigorously using real-world performance metrics, not just offline benchmarks.
• Design end-to-end ML pipelines: Data ingestion → feature generation → training → validation → deployment → monitoring → retraining
• Build reliable inference pipelines that support near-real-time and batch workflows.
• Implement: Model versioning, Automated retraining, Drift detection, Performance monitoring.
• Work closely with product and platform engineers to integrate ML outputs into customer-facing systems.

Requirements:

• 5- 7+ years of experience in data engineering, ML engineering, or applied ML roles.
• Proven experience deploying ML systems into production (not just notebooks).
• Strong background in time-series data (forecasting, anomaly detection, temporal feature engineering).
• Deep proficiency in Python and modern data/ML libraries.
• Experience building scalable data pipelines (batch and streaming).
• Strong systems thinking — ability to reason about end-to-end data and model lifecycles.
• Leadership experience.

It´s a Plus:

• Experience with Databricks, Spark, or similar large-scale data platforms.
• Geospatial data experience (GIS, raster/vector data, spatial joins, map-based features).
• Experience in weather, energy, load forecasting, or infrastructure modeling.
• Familiarity with MLOps frameworks and best practices.
• Experience working with messy, real-world datasets and ambiguous problem statements.
• Exposure to hybrid physics + ML systems or domain-constrained modeling.