Software Engineer III, ML Model Delivery

What you'll do

• Design, build, and maintain platform components for the end-to-end machine learning model lifecycle.
• Develop and maintain data and feature pipelines to feed production ML models.
• Build and manage cloud-based infrastructure on AWS including Databricks, EMR, ECS, and S3.
• Automate model deployment, testing, and release processes within the MLOps toolchain.
• Migrate legacy machine learning workloads to cloud-native, scalable platforms with zero downtime.
• Monitor platform health and resolve performance or stability issues in serving infrastructure.
• Utilize AI-assisted development tools to improve code quality, refactoring, and documentation.
• Translate data scientist requirements into reliable technical platform capabilities.

What we're looking for

• Formal training or certification in software engineering and 3+ years of applied experience.
• Hands-on experience building and maintaining production data or ML pipelines.
• Experience using enterprise-authorized AI-assisted software development tools with the ability to validate outputs.
• Understanding of responsible AI use, including data sensitivity and secure handling of inputs/outputs.
• Proficiency in Python and experience with ML libraries like Pandas, NumPy, and Scikit-learn.
• Working knowledge of cloud platforms, particularly AWS, and cloud-native development patterns.
• Practical experience with infrastructure-as-code and deployment automation (Terraform preferred).
• Experience with Databricks, MLOps practices, AWS certifications, RAG architectures, or container-based deployment (preferred).