Principal Engineer, AI/ML Software

Duties

• Design, build, and maintain robust MLOps (Machine-Learning Operations) software systems.
• Support the development, deployment, testing, and monitoring of AI/ML models on modern cloud-native platforms.
• Collaborate with data scientists, software engineers, and stakeholders to operationalize AI/ML solutions and ensure their production readiness.
• Implement and maintain ETL pipelines, automated workflows, and scalable data stores.
• Ensure high standards of model performance, security, and scalability through continuous monitoring and enhancement of software infrastructure.
• Guide the MLOps technology roadmap and evaluate emerging tools and technologies to enhance platform capabilities.
• Utilize MLOps frameworks such as Kubeflow and MLflow, and work with containerization and orchestration tools including Docker and Kubernetes.
• Deploy infrastructure using Terraform and manage cloud-based resources on platforms such as GCP, AWS, and Azure.
• Contribute to Agile development processes and cross-functional team collaboration.

Requirements

• Must have a Bachelor’s degree in Computer Science, Information Technology, or a related field (or foreign education equivalent) and five (5) years of experience as a software engineer building and maintaining machine learning software workflows.
• In the alternative, must have a Master’s degree in Computer Science, Information Technology, or a related field (or foreign education equivalent) and three (3) years of experience as a software engineer building and maintaining machine learning software workflows.
• Must possess the following (quantitative experience requirements not applicable to this section):
• Demonstrated Expertise (“DE”) designing, developing, and maintaining end-to-end machine learning (ML) pipelines, including data ingestion, preprocessing, model training, validation, and deployment (using PyTorch or TensorFlow); and managing experiment tracking and model lifecycle with MLflow or CometML;
• DE in technical leadership of production ML platforms and pipelines—leading a small, cross-functional team; setting standards, running design/code reviews, and mentoring junior engineers;
• DE building scalable systems on cloud platforms, with hands-on experience designing fault-tolerant architectures, distributed training setups, multi-cloud strategies (using AWS, GCP, or Azure), and automating infrastructure tasks with Linux and shell scripting;
• DE in containerization, orchestration, and MLOps/DevOps practices, including deploying ML models and pipelines with Docker and Kubernetes; implementing CI/CD and infrastructure-as-code (Terraform or AWS CloudFormation); and setting up monitoring and observability (Prometheus and Grafana);
• DE developing distributed data processing pipelines for real-time or batch ML workflows (using Apache Airflow and Apache Kafka);
• DE leading the design, building, and maintenance of scalable, robust, and secure RESTful APIs and microservices architectures using Python, with knowledge of computer networks and protocols.