Cloud Application Engineer

Trabus Technologies•San Diego, CA

19d•$110,000 - $135,000

About The Position

TRABUS is seeking a Cloud Application Engineer to support secure backend systems, AI/ML infrastructure, and cloud-native development for Naval Sea Systems Command (NAVSEA) and the COLUMBIA Submarine Program Office (PMS 397). This is a hands-on technical role working across backend engineering, DevSecOps, and MLOps within secure DoD environments. You'll collaborate with data scientists, engineers, and cybersecurity teams to build high-performing, scalable, and compliant systems that power data-intensive and AI/ML-enabled applications in support of these critical Navy programs.

Requirements

Active Secret Security Clearance.
Bachelor's degree in Computer Science, Engineering, IT, Data Science, or related field (or equivalent experience).
2+ years' combined experience in backend engineering, DevOps/DevSecOps, or infrastructure engineering.
Strong experience with: Python, Bash SQL (PostgreSQL, MySQL) and NoSQL (Redis, MongoDB, Cassandra) API technologies (FastAPI, GraphQL, REST, Swagger, Postman) Docker, Kubernetes Git, GitLab CI, GitHub Actions, Jenkins Terraform, Ansible, Helm Solid understanding of Linux/Unix systems and DoD cybersecurity practices.

Nice To Haves

Master's degree in a STEM field
Experience with Kubeflow, MLflow, Airflow, Ray
Experience with GPU clusters and containerized GPU workloads
AWS or Azure Government Cloud certifications
Strong communication and documentation abilities
Attention to detail and ability to juggle multiple priorities
Experience in Agile environments

Responsibilities

Design and develop APIs and backend services using Python, FastAPI, GraphQL, REST.
Build data ingestion pipelines and data models for geospatial, time-series, and large datasets.
Develop scalable cloud and serverless applications (AWS, GovCloud, Azure Government, DigitalOcean).
Integrate backend systems with AI/ML models and analytics workflows.
Build and maintain secure environments for AI/ML training, experimentation, and production deployment.
Support ML tools/frameworks: MLflow, Kubeflow, TensorFlow, PyTorch, Hugging Face.
Operationalize ML models in containerized and GPU-enabled environments.
Implement secure CI/CD pipelines with integrated security scanning.
Automate infrastructure with Terraform, Ansible, Helm, and Kubernetes.
Support compliance with RMF, STIGs, NIST 800-53, and other DoD cybersecurity requirements.
Build secure architectures in AWS GovCloud, Azure Government, or DoD cloud.