Machine Learning Engineer

• Develop, build and maintain infrastructure required for machine learning, including data pipelines, model deployment platforms, and model monitoring.
• Develop and maintain tools and libraries to support the development and deployment of machine learning models.
• Automate machine learning workflows using DevSecOps principles and practices.
• Collaborate with business and technology stakeholders to understand current and future ML requirements
• Design and develop innovative ML models and software algorithms to solve complex business problems in both structured and unstructured environments
• Design, build, maintain and optimize scalable ML pipelines, architecture and infrastructure
• Adapt machine learning to areas such as virtual reality, augmented reality, object detection, tracking, classification, terrain mapping, and others.
• Deploy ML models and algorithms into production and run simulations for algorithm development and test various scenarios
• Automate model deployment, training and re-training, leveraging principles of agile methodology, CI/CD/CT (Continuous Integration/ Continuous Deployment/ Continuous Training) and MLOps Enable model management for model versioning and traceability to ensure modularity and symmetry across environments and models for ML systems
• Collaborate with development and operations teams to implement software solutions that improve system integration and automation of ML pipelines.
• Design, develop, and manage data flows and APIs between upstream systems and applications.
• Troubleshoot and resolve issues related to system communication, data flow, and data quality.
• Collaborate with technical and non-technical teams to gather integration requirements and ensure successful deployment of data solutions.
• Create and maintain comprehensive technical documentation of software components.
• Work with IT to ensure systems meet evolving business needs and comply with data governance policies and security requirements.
• Implement and enforce the highest standards of data quality and integrity across all data processes.
• Manage deliverables through project management tools.

• 3+ years of experience in developing and deploying machine learning models in a production environment.
• 3+ years of experience in programming with Python
• 2+ years of hands-on experience utilizing Google Cloud Platform (GCP) services, including BigQuery and Google Cloud Storage to efficiently manage and process large datasets, as well as Cloud Composer and/or Cloud Run.
• Experience with version control systems like GitHub for managing code repositories and collaboration.
• 2+ years of experience with code quality and security scanning tools, such as, SonarQube, Cycode and FOSSA.
• 3+ years of experience with data engineering tools and technologies, such as, Kubernetes, Container-as-a-Service (CaaS) platforms, OpenShift, DataProc, Spark (with PySpark) or Airflow.
• Experience with CI/CD practices and tools, including Tekton or Terraform, as well as containerization technologies like Docker or Kubernetes.
• Excellent problem-solving and analytical skills, with a focus on data-driven solutions.
• Familiarity with cloud computing platforms like AWS, Azure, or Google Cloud Platform.
• Familiarity with Atlassian project management tools (e.g., Jira, Confluence) and agile practices.

• 5+ years of experience in the automotive industry, particularly in auto remarketing and sales.
• Proven ability to thrive in dynamic environments, managing multiple priorities and delivering high-impact results even with limited information.
• Exceptional problem-solving skills, a proactive and strategic mindset, and a passion for technical excellence and innovation in data engineering.
• Demonstrated commitment to continuous learning and professional development.
• Familiarity with machine learning libraries, such as TensorFlow, PyTorch, or Scikit-learn
• Experience with MLOps tools and platforms.

• Bachelor's degree in Computer Science, Information Systems, or a related field.

• Master's degree in a relevant field (e.g., Computer Science, Data Science, Engineering).

Stefanini