ML Infrastructure Engineer

Skills

Design develop and manage robust machine learning infrastructure on a public cloud platform (experience with Google Cloud Platform (GCP) is strongly preferred). Collaborate with data scientists to translate their models into production-ready ML pipelines. Develop and implement machine learning pipelines using programming languages like Java Python or Scala (proficiency in both is a plus). Integrate ML pipelines with data storage warehousing and compute resources within the cloud environment. Optimize ML pipelines for performance scalability and resource efficiency. Deploy and manage ML models in production using containerization technologies and orchestration tools. (Experience with Kubernetes and Kubeflow is a plus) Leverage ML Operations (MLOps) frameworks/solutions to streamline the deployment monitoring and management of machine learning models. Monitor and maintain the health and performance of the ML infrastructure. Apply DevOps principles for automation and continuous integration/continuous delivery (CI/CD) of ML pipelines. Stay up-to-dat on the latest advancements in machine learning frameworks and tools (TensorFlow PyTorch scikit-learn etc.). Document technical specifications and best practices for building and deploying ML models. Work collaboratively in a cross-functional team to deliver successful data-driven solutions.

Description

We are seeking an experienced Senior Engineer, Analytics with a focus on Machine Learning to join our team. As a Senior Engineer, you will play a key role in designing, building, and maintaining our machine learning infrastructure. You will work closely with our data scientists, product managers, and other engineers to develop and deploy scalable, efficient, and reliable machine learning models.

Responsibilities

• Bachelor's degree in Computer Science or a related field
• 5+ years of experience in software engineering, with a focus on machine learning infrastructure
• Proficient in programming languages such as Java, Python or Scala
• Experience with a public cloud provider, with a strong preference for GCP
• Familiarity with ML frameworks such as TensorFlow, PyTorch, and ML libraries such as scikit-learn
• Knowledge of DevOps principles and practices
• Understanding of containerization technologies such as Docker and Kubernetes
• Experience with VertexAI is strongly preferred
• Excellent problem-solving skills, with the ability to work independently and collaboratively
• Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders
• Certification in a public cloud provider or a relevant technology is a plus
• Experience with data warehousing and data lake technologies is a plus

Educational Requirements

• Experience in Machine Learning engineer or Infrastructure roles, with a focus on Machine Learning infrastructure.
• Proficiency in programming languages Python, Java
• Experience with cloud platforms such as AWS, GCP, or Azure. GCP experience is strongly preferred.
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn).
• Knowledge of DevOps principles and tools (e.g., CI/CD pipelines, Terraform).
• Strong understanding of Containerization technologies (e.g., Docker, Kubernetes).
• Excellent problem-solving and communication skills.
• Experience with big data technologies such as Apache Spark or Hadoop Preferred.