What You'll Do
- Engineer the data pipelines that feed AI workloads — ingestion, transformation, and serving for model training and real-time digital twin event streams
- Automate MLOps and CI/CD for AI workloads: reproducible builds, test automation, deployment, monitoring, and rollback
- Integrate multi-agent AI orchestration with platform services and external systems through APIs and agent-tool protocols such as MCP
- Build compliance into the pipeline — automated control validation and evidence generation that shorten ATO cycles
- Partner with platform engineers and product leads to turn AI capabilities into reusable components
What You'll Bring
- 8+ years of professional software engineering experience, including designing and operating production distributed systems
- Hands-on experience with distributed ML and data engineering tooling (for example, Apache Spark/MLlib, Kafka, NiFi, or Airflow)
- Experience deploying and operating containerized workloads on a major cloud platform (AWS preferred; Kubernetes/EKS or Red Hat OpenShift a plus)
- Proficiency in Python
- Experience with CI/CD pipeline automation and pipeline-as-code