Machine Learning Engineer

• Develop NLP models for information search and recommendation within legal documents, with a particular focus on Spanish
• Maintain and monitor models in production
• Design conversational agents to help lawyers across legal domains by ensuring accurate answers and streamlining drafting tasks
• Collaborate with engineers, product managers, and product designers to identify product development opportunities
• Contribute to internal best practices and help shape Engineering strategy
• Participate in the Data Science cross-functional chapter and its internal projects
• Share knowledge, review scientific papers, and support continuous improvement
• Experiment with new tools and propose process improvements
• Participate in recruitment through technical assessments or interviews

Experienced Machine Learning Engineer, Spanish Speaking

• Spend 50% of your time on R&D projects within the Innovation team and 50% on client projects within the Data & Transformation Services team
• Translate business needs into technical problems and define measurable objectives
• Work with senior team members and managers to identify appropriate technical solutions
• Conduct AI research
• Collaborate with data engineers to collect, clean, and preprocess data
• Conduct exploratory analyses using relevant visualizations
• Develop and deploy machine learning models
• Optimize existing models and design new approaches
• Follow coding best practices to industrialize models with Solution experts
• Present technical findings to the team and clients
• Collaborate with different teams to integrate solutions
• Share knowledge internally and provide expert support
• Keep up to date with technological advances and industry trends in data science and machine learning

AI/ML Engineer – 50% Client Projects, 50% R&D

• Develop and deploy predictive models powering commodity, energy, and maritime intelligence platforms
• Design, build, and maintain production-grade machine learning workflows and microservices for power market forecasting and electricity grid modeling
• Transition statistical and machine learning prototypes into scalable, production-ready Python applications
• Design and optimize PostgreSQL schemas for high-throughput time-series data, event streams, and normalization routines
• Establish automated model training, backtesting, evaluation, tuning, and feature/model versioning standards
• Construct clean data ingestion and transformation pipelines with high integrity, validation, and low-latency access
• Write modular, well-tested Python code
• Participate in peer code reviews, CI/CD automation, and Agile delivery processes
• Collaborate with Data Scientists, Data Engineers, and Product teams

ML Engineer – Power

• Design and implement machine learning and AI solutions for catalog quality, including product categorization, qualification, attribute extraction, product matching, and enrichment
• Build and maintain scalable pipelines for product classification, entity matching, semantic similarity, and attribute extraction across a large and continuously evolving product catalog
• Develop, adapt, and evaluate machine learning models, including LLMs and vision-language models, for domain-specific catalog tasks
• Design pragmatic human-in-the-loop and automated workflows combining machine learning, AI models, rules, and internal data sources
• Write production-ready code and deploy AI systems in a live environment at scale
• Define and track evaluation metrics for catalog quality and model performance; create offline benchmarks, run experiments, and communicate results
• Partner with software engineers, product managers, and business stakeholders to frame problems from scientific and business perspectives
• Investigate and fix production issues; ensure reliability, observability, and performance of AI systems
• Stay engaged with developments in machine learning, Generative AI, information extraction, entity resolution, and scalable ML systems

Senior Machine Learning Engineer

• Design, build, and optimize the agentic stack powering conversational and autonomous agents
• Architect and maintain end-to-end LLM features, including model orchestration, routing, monitoring, logging, and evaluation
• Develop and improve ML infrastructure, including observability, load balancing, scaling patterns, and deployment workflows
• Build robust datasets and evaluation pipelines for deterministic systems (ASR) and non-deterministic systems (LLMs and agents)
• Collaborate with Software Engineers to establish scalable, observable, and automated infrastructure around ML services
• Work with Product Managers to assess feasibility, refine specifications, and reliably deliver ML features
• Contribute to technical strategy, including model and tool selection, architectural decisions, and the agentic platform roadmap
• Stay current with research, frameworks, and open-source developments in LLMs, agentic systems, and evaluation methodologies