• Construção de pipelines de Machine Learning de ponta a ponta: incluindo preparação de dados, otimização de código, automatização de rotinas de treino e predição de modelos; • Monitoramento de métricas de qualidade, eficiência, custo e uso de produtos de dados; • Liderança técnica no processo de MLOps do time, definindo boas práticas de desenvolvimento e sustentação de soluções; • Desenvolvimento de arquiteturas em nuvem para soluções de dados e IA (GCP, AWS, Azure); • Construção de pipelines de dados com modelos de Gen AI embarcados (ex: preparação de dados não estruturados para estruturados); • Desenvolvimento de pilotos junto à área para prototipação de conceitos e prova de valor.

• Own the full lifecycle of predictive models in production — architecture, training pipelines, inference infrastructure, deployment, and ongoing model health • Build and operate the systems that route model outputs into live product surfaces: search ranking, recommendations, feed ordering, and related user-facing experiences • Establish and maintain model monitoring, alerting, drift detection, and retraining cadences — the feedback loops that keep deployed models accurate over time • Partner closely with Data Science, Data Engineering, Product Management, and backend engineering to move work from validated approach to production system • Own the decision-making process on whether to leverage ML infrastructure & expertise from our parent company, GOAT Group, and when to advocate for building in-house solutions. • Contribute to ML infrastructure decisions — serving architecture, feature computation, pipeline orchestration — with an eye toward what scales as the team and model count grows • Set technical standards and raise the bar for how ML systems are built, evaluated, and operated across the pod

• Design, develop, optimize and deploy ML models, including large language models, for various natural language processing tasks. • Implement and maintain training pipelines, leveraging distributed training and optimizing for performance and efficiency. • Collaborate with cross-functional teams to gather requirements, define model architectures and iterate on model development. • Conduct model evaluations, performance analysis, and optimization to improve model accuracy and reduce biases. • Stay up-to-date with the latest research and advancements in the field of natural language processing, multimodal signals, and large language models. • Contribute to the development of best practices, guidelines, and ethical AI principles for responsible ML development and deployment.

• Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) • Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. • Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX • Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. • Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. • Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team.