Manager ML Engineer

Job Description

As the Manager, Machine Learning Engineer you will be responsible for leading a team of talented ML engineers, guiding them in the design, development, and deployment of machine learning models. You will collaborate with cross-functional teams to ensure the successful integration of ML solutions into our products and services. Key Responsibilities: Lead and mentor a team of machine learning engineers, providing guidance and support in their professional development. Oversee the design and implementation of machine learning models using Python and relevant libraries (e.g., TensorFlow, PyTorch, scikit-learn). Manage the deployment and scaling of ML models on AWS infrastructure, utilizing services like SageMaker, EC2, and Lambda. Implement Infrastructure as Code (IaC) using Terraform to automate provisioning and configuration of ML environments. Collaborate with data scientists, product managers, and other stakeholders to align ML solutions with business objectives. Ensure the reliability, scalability, and performance of deployed models through continuous monitoring and optimization. Stay informed about the latest advancements in machine learning and AI technologies, and drive innovation within the team. Document processes and methodologies to ensure reproducibility and knowledge sharing across the organization. Qualifications: Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent experience. Proven experience as a Machine Learning Engineer, with a track record of leading teams and projects. Strong proficiency in Python and machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn). Extensive experience with AWS services for deploying and managing ML models. Expertise in Terraform for infrastructure automation. Solid understanding of machine learning algorithms and techniques. Excellent leadership, communication, and collaboration skills. Preferred Qualifications: Master's degree or Ph.D. in a related field. Experience with other cloud platforms (e.g., Azure, Google Cloud). Knowledge of big data technologies (e.g., Hadoop, Spark). Familiarity with deep learning architectures and frameworks. Experience with version control systems like Git.