• Educate and evangelize AI to boost productivity across all internal groups • Build scalable, high-availability models from claims, contracts, employer and price transparency data to create intelligence and handle real-time inference requests • Manage and optimize the underlying cloud infrastructure for both large-scale model training and high-throughput serving environments • Implement advanced monitoring and alerting systems to track model performance, data drift, and prediction latency in production • Establish processes for model versioning, lineage tracking, and rollback capabilities to ensure operational integrity and auditability • Collaborate directly with the Data Science team to containerize model code, optimize models for production serving efficiency, and transition models from research to production-readiness • Automate all aspects of the model lifecycle, from data ingestion and feature engineering to deployment and retraining triggers

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description To be part of this fast–growing fintech and join the team responsible for implementing the data strategy for the company, and delivering tangible results to support and implement Baubap’s mission. As a Senior Machine Learning Engineer in Baubap you will work closely with the business and operational units, understand the problems that we want to solve, propose and implement data- and Machine Learning-driven solutions. In addition, you will be able to measure and communicate the impact of Machine Learning models on business. - Successfully develop and implement machine learning models that optimize financial processes, resulting in a significant improvement in efficiency and profitability. - Improving and building new ML models into production for new project lines. - Generate new insights to be able to have better understanding of the clients using ML. - Research and have fast insights in order to make improvements to the actual ML models implemented in Baubap. - Optimize and refine models to achieve desired accuracy and efficiency. Day to day tasks include: - Understand, propose, design, implement, and bring Machine Learning solutions into production to address business problems. - Create POC’s researching exhaustively the viability to find new features, data sources and/or algorithm optimizations. - Propose creative new initiatives that can improve our Credit Risk Models. - Coordinate with Data Engineers to ensure smooth and successful implementation of pipelines for the new features. - Collaborate with engineering and product teams to integrate and optimize machine learning algorithms within the company's applications and systems. - Evaluate, enhance, and maintain existing machine learning models, and propose continuous improvements. Qualifications - Proficiency in Python. - Proficiency in MySQL. - Strong Experience with the complete Machine Learning Pipeline (Feature engineering, ML model training, evaluation and monitoring). - Experience in projects using AWS like S3, EC2, GLUE, Lambda. - Experience in projects using NLP, Face Recognition, ML recommenders, Image Pre-processing. Benefits - Being part of a multicultural, highly driven team of professionals. - 20 vacation days / year + 75% holiday bonus (Prima Vacacional). - 1 month (proportional) of Christmas bonus (Aguinaldo). - Food vouchers. - Health & Life insurance. - Competitive salary.

• Lead the end-to-end machine learning lifecycle, including data ingestion, model development, training, evaluation, deployment, monitoring, and iteration • Own and evolve ML Ops architecture, including CI/CD for models, feature pipelines, model versioning, monitoring, and retraining strategies • Serve as a player-coach, contributing directly to design reviews, code reviews, prototyping, and implementation for high-impact initiatives • Architect and deploy computer vision models (e.g., CNNs, transformers, multimodal models) and LLM-based systems (e.g., prompt pipelines, fine-tuning, RAG, evaluation frameworks) • Establish and track model performance, reliability, scalability, and ethical AI metrics, ensuring compliance with internal standards and regulatory expectations • Lead project planning, feasibility analysis, and technical scoping for new AI-driven functionality • Identify technical risks early and develop mitigation and contingency plans • Maintain and design software primarily in Python, with the ability to contribute to or review code in C/C++, Java, JavaScript, C#, and mobile frameworks as needed • Ensure strong testing discipline, including unit testing, integration testing, and test-driven development (TDD) practices • Apply and mentor on software design patterns, debugging strategies, and production troubleshooting best practices • Collaborate cross-functionally to align priorities, manage dependencies, and communicate progress, tradeoffs, and outcomes • Write and present technical and executive-level status updates to stakeholders • Oversee software and model releases and communicate release readiness and impact

• Lead the design and delivery of analytics-ready data models and transformation layers using dbt as the standard framework • Establish and enforce dbt development standards, including model design, documentation, testing, CI/CD, and release practices • Own delivery and operations of scalable ingestion, transformation, and delivery pipelines on AWS, ensuring reliability, performance, and cost efficiency • Partner with Cloud Engineering and Security to ensure AWS data solutions meet security, privacy, and compliance requirements • Implement monitoring, alerting, incident response practices, and runbooks for dbt and AWS workloads to improve operational stability • Drive strong data quality practices including source definitions, freshness checks, automated tests, and data lineage expectations • Collaborate with business stakeholders to translate needs into prioritized roadmaps and delivered data products • Manage and mentor data engineers and analytics engineers through coaching, performance management, and career development • Promote disciplined engineering practices across the team including code review standards, documentation, automation, and reusable frameworks • Enable future machine learning use cases by ensuring curated datasets are ML-ready, including feature readiness and foundational requirements for model operationalization • Evaluate and introduce platform improvements that strengthen scalability, maintainability, governance, and developer productivity