Machine Learning Engineer

• Develop, train, and deploy machine learning models using advanced techniques
• Collaborate with the data engineering team to integrate models into production systems
• Add observability layers for the models developed in the company’s data lake
• Develop Generative AI agents to accelerate the company’s processes
• Monitor and optimize model performance in production
• Apply MLOps techniques to improve the efficiency and scalability of machine learning workflows
• Document processes, models, and results clearly and accessibly
• Participate in code reviews and promote development best practices

Machine Learning Engineering Analyst – Mid-Level

• Develop RAG and agent memory solutions for production environments
• Optimize semantic search using embeddings and reranking techniques, such as bi-encoders and cross-encoders
• Build and evolve context-retrieval pipelines focused on scalability, availability, and performance
• Conduct experiments and benchmarks to evaluate and compare models
• Develop solutions according to engineering best practices, including testing, CI/CD, IaC, and observability
• Contribute to technical and architectural decisions related to the Generative AI platform
• Work with Generative AI, Machine Learning, software engineering, and cloud technologies
• Work across the full lifecycle, from model experimentation and evaluation to building robust production solutions
• Make an impact on the bank’s digital products at scale

Senior Machine Learning Engineering Analyst – Artificial Intelligence

• Collect, clean, and organize relevant datasets
• Perform exploratory analysis to identify patterns and trends applicable to machine learning projects
• Design, implement, and evaluate machine learning algorithms
• Perform feature selection, hyperparameter tuning, and model validation
• Create training and test datasets, train models, and evaluate their effectiveness using accuracy, precision, and recall
• Implement cross-validation to ensure robustness
• Use visualization tools to communicate insights clearly
• Explore correlations and provide actionable recommendations based on detected patterns
• Optimize existing models to improve accuracy, efficiency, and scalability
• Collaborate with multidisciplinary teams and provide technical support in machine learning and data analysis
• Keep code and data repositories organized and up to date

• Design and build the ML engineering for the pricing engine
• Develop inference serving, training pipelines, and feature engineering for real-time, low-latency models on Ray
• Design chained model pipelines on Ray Serve, including composition, low latency, and update strategies
• Build distributed training pipelines with Ray Train/Data, HPO with Ray Tune, and tenant-specific trained models with resilient checkpointing
• Define and materialize features in the Feast/Redis feature store, ensuring consistency between training and production
• Implement and optimize linear programming and offline RL components of the pricing pipeline
• Monitor modeling drift, validate versions, and deliver explainability with SHAP in partnership with MLOps
• Serve as a technical reference, mentor team members, and define viable, scalable solutions
• Manage handoffs with data scientists, receive data from data engineers, and deliver to the MLOps/Platform team for deployment and operations

Senior Machine Learning Engineer

• Support the development and evolution of Machine Learning and Artificial Intelligence models
• Participate in data preparation, analysis, and processing
• Assist with the validation and monitoring of models in production
• Collaborate with multidisciplinary teams to identify opportunities for applying AI
• Contribute to the documentation and continuous improvement of model development processes
• Support experiments and analyses to generate data-driven insights
• Work closely with engineering, data, and product teams

Machine Learning Developer

• Develop, deploy, and continuously improve machine learning models for Zé Delivery.
• Build and maintain data, feature, training, and inference pipelines at scale.
• Develop recommendation and personalization solutions for different stages of the customer journey.
• Monitor the performance, quality, and stability of models in production.
• Define and analyze experiments, A/B tests, and impact metrics.
• Partner with Data, Engineering, Product, and Business teams.
• Serve as a technical reference and support the development of more junior professionals.
• Contribute to an inclusive, diverse, collaborative, and technology-driven culture at Ambev.

• Work within the Data Platform organization, as part of a strategic squad responsible for developing, operating, and scaling Machine Learning and Artificial Intelligence capabilities
• Architect and propose solutions aligned with the AWS-first strategy
• Influence, champion, and promote platform capabilities across technology and business teams
• Design, build, and evolve MLOps and AI/GenAI infrastructure and platforms on AWS
• Ensure robustness, high availability, observability, and cost control
• Lead the transition and expansion of MLOps into an AI Platform, including GenAI Ops and governance of generative models
• Implement automated CI/CD pipelines for ML/AI models
• Ensure secure, reproducible, and monitored production deployments
• Ensure security, compliance, quality, and traceability standards for models and data throughout their lifecycle
• Lead architecture discussions and defend technical decisions based on trade-offs
• Influence stakeholders without relying on formal authority
• Promote the data platform, identify pain points across other teams, and present solutions to increase adoption of its tools

Staff AI/MLOps Engineer

• Build, train, validate and deploy machine learning models driving credit, fraud, risk, personalization and AI-native experiences
• Frame ambiguous business problems as well-defined modeling problems
• Engineer and maintain features and datasets for training and inference
• Deploy and maintain ML models in batch and real-time scenarios
• Integrate models with other systems and monitor operational and business metrics
• Lead modeling projects end-to-end, from problem framing and stakeholder alignment through delivery, monitoring and iteration
• Contribute to the design, documentation, maintenance and optimization of modeling codebases, platforms and tooling
• Translate business needs into modeling strategies aligned with Nubank’s architecture and long-term goals
• Partner with technical and business stakeholders to define strategies and deliver high-impact models
• Share knowledge, mentor peers and contribute to ML and data literacy initiatives
• Work with large-scale training and experimentation pipelines, feature stores, distributed data processing, AWS, Kubernetes, CI/CD, experiment tracking, model versioning and modern ETL/ELT practices

Lead Machine Learning Engineer

• Own the full model lifecycle — feature engineering, training, validation, registration, and deployment
• Monitor models in production and lead retraining when performance degradation or drift occurs
• Maintain and evolve CI/CD pipelines that promote models and pipelines across development, staging, and production environments
• Automate and productionize model and agent evaluation pipelines in support of Data Science
• Build and maintain reusable internal metrics packages for standardized experiments
• Operate the infrastructure for fine-tuning language models
• Administer the AI Gateway, including model routing, rate limits, and cost control and attribution
• Ensure feature, experiment, and model governance is aligned with Afya policies
• Serve as the technical bridge between Data Science, AI Engineering, Data Engineering, and SRE
• Apply scalability, security, and cost-efficiency practices to ML/AI infrastructure