Principal Platform Engineer - Connected Aviation (Remote)

Collins Aerospace is a leader in aviation technology and is seeking a Principal Platform Engineer for their Aviation Intelligence team. In this role, you will lead critical projects related to AI/ML, data science, and data management, while managing AWS infrastructure and collaborating with various engineering teams.

Responsibilities:

• Own and evolve AWS infrastructure supporting data platform, ML training, and analytics workloads (Iceberg/Trino, ETL pipelines, Kubeflow/MLflow)
• Design, deploy, and maintain EKS-based services and Kubernetes workloads
• Build and manage Terraform infrastructure across environments (dev/staging/prod)
• Design and maintain CI/CD pipelines for infrastructure and application deployment (GitLab/GitHub)
• Operate and improve Kafka/Redpanda clusters
• Improve reliability, observability, and performance of prediction services
• Support Dagster for data workflow orchestration
• Collaborate with Data Science, ML Engineering, and Data Engineering to productionize models and data pipelines
• Strengthen AWS IAM, networking, and connectivity between cloud and on-prem systems
• Support cyber hardening efforts with our RTX cyber team
• Identify and incrementally improve existing infrastructure and deployment patterns

Requirements:

• Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and minimum 8 years prior relevant experience or an Advanced Degree in a related field and minimum 5 years of experience
• Proficiency with Python
• Strong experience with AWS (EKS, IAM, VPCs, networking)
• Hands-on Kubernetes experience operating production workloads
• Experience managing Kafka (or Redpanda) in production environments
• Proficiency with Terraform for infrastructure as code
• Experience building and maintaining CI/CD pipelines (GitLab, GitHub Actions, or similar)
• Solid understanding of distributed systems, reliability, and scaling
• Experience supporting production data pipelines or ML systems
• Experience with observability stacks (Prometheus, Grafana, Thanos, etc.)
• Experience with Kafka Connect
• Experience with Rust or a willingness to learn it
• Familiarity with Dagster, Airflow or other modern ETL orchestration tools
• Background supporting ML/AI systems in production (MLOps)
• Experience optimizing infrastructure for performance and cost
• Familiarity with Iceberg, Spark, Elasticsearch and other large-scale data processing systems
• Experience working in hybrid infrastructure environments (cloud + on-prem Kubernetes)