Act as a technical reference in MLOps, promoting best practices among data science and engineering teams.
Collaborate cross-functionally with data scientists, engineers, architects and product squads to ensure the delivery of robust, scalable solutions.
Take a proactive stance in problem-solving, contributing to the continuous improvement of processes, tools and ML architecture.
Support incidents in staging and production environments, leading root-cause identification, fast remediation and improvement recommendations.
Demonstrate autonomy and ownership in defining deployment strategies, model architecture and monitoring.
Translate technical and business needs into practical, sustainable production solutions.
Promote knowledge sharing, contributing to the team's technical growth and fostering a culture of operational excellence.
Maintain a product mindset and a systemic view of the full lifecycle of ML models in production.

Proven experience developing and deploying machine learning models on cloud platforms such as GCP, Azure, or AWS.
Strong command of CI/CD practices applied to the ML lifecycle, using tools such as Jenkins, Azure DevOps Pipelines, Cloud Build, among others.
Hands-on experience using Airflow to automate batch pipelines.
Experience orchestrating online model serving via APIs using Kubernetes (K8s) and managed services like Cloud Run.
Ability to build end-to-end pipelines for retraining, deployment and monitoring using tools like Kubeflow, Vertex AI Pipelines, etc.
Experience deploying containerized models at scale in cloud environments, including defining autoscaling parameters and resource allocations.
Familiarity with canary, blue/green and shadow deployment strategies to minimize production risk.
Advanced knowledge of model and infrastructure monitoring tools such as Prometheus, Grafana, Rancher and custom dashboards.
Ability to design cloud ML architectures aligned with project requirements, considering cost, performance and scalability.
Experience implementing streaming data pipelines for real-time ingestion, processing and consumption.
Solid understanding of MLOps principles, including versioning, lineage, testing and model lifecycle management.
Desirable experience building internal tools or frameworks (such as FenixAI) to standardize and empower teams' autonomy over ML infrastructure.

Senior Data Scientist

Key skills