Apply advanced software engineering skills to take AI models and prototypes into secure, scalable, maintainable production applications.

Develop and maintain CI/CD pipelines for ML applications
Automate infrastructure provisioning and configuration using modern DevOps tools
Implement containerization and orchestration for ML workloads
Support reliability, security, and compliance in ML Ops environments
Collaborate with cross-functional teams to optimize deployment and monitoring
Design, develop, deploy, and maintain AI-enabled applications and backend services
Build REST APIs and integrate LLMs, AI models, and enterprise data systems
Transform AI models and prototypes into production-ready applications with secure, scalable, maintainable operations
Work with data scientists and ML engineers to ensure efficient operations across the ML lifecycle
Key scope includes software engineering with applied AI expertise

Bachelor’s Degree and 5 years of relevant exempt experience
Master’s Degree and 3 years of relevant professional experience
Ph.D. and 0 years of experience
One acceptable equivalency option may be used as an alternative to a Bachelor’s Degree:
- NNS Apprentice School graduate
- Navy Nuclear Power School (NNPS) graduate
- Associate’s Degree or other formal 2 year program plus 2 years of relevant exempt experience, or 4 years of relevant non-exempt experience
- Military Paygrade E-5 or above military experience
- High School/GED plus 4 years combined of Manufacturing, Shipbuilding, Trades, Military experience or other relevant exempt experience
- High School/GED plus 8 years combined of Manufacturing, Shipbuilding, Trades, Military experience or other relevant non-exempt experience
- A relevant professional certification can substitute for a Bachelor’s Degree
Strong Python programming and REST API development skills
Experience with software architecture, cloud development, Git, and automated testing
Familiarity with AI/ML concepts and practical integration of LLMs or AI services
Strong analytical, troubleshooting, and collaboration skills
Bachelor’s degree in Computer Science, Engineering, or related field

Experience supporting ML Ops workflows and machine learning deployments
Familiarity with ML Ops tools: MLflow, Kubeflow, Airflow
Knowledge of security best practices for ML and cloud environments
Experience with large-scale data processing systems: Spark, Hadoop
Strong scripting skills (Python, Bash)
Ability to collaborate in a fast-paced, cross-functional environment
Strong proficiency with CI/CD tools: Jenkins, GitLab CI, Azure DevOps
Hands-on containerization and orchestration: Docker, Kubernetes
Familiarity with cloud platforms: AWS, Azure, GCP
Knowledge of infrastructure-as-code tools: Terraform, Ansible
Experience with monitoring and logging: Prometheus, Grafana, ELK stack
Solid understanding of version control systems (Git)
Technical skills highly preferred across:
- Python: backend and application development
- API development: REST APIs, FastAPI, Flask or similar frameworks
- Software architecture: scalable, modular, maintainable application design
- Cloud development: Azure, AWS, or comparable platforms
- Applied AI: LLM integration, generative AI APIs, and RAG implementations
- DevOps: Git, CI/CD, automated testing, and containerization

AI Software Engineer

Job Description