DEVOPS ENGINEER

Huntington Ingalls•Newport News, VA

42d•Hybrid

About The Position

Design, develop, and test operating systems-level software, compilers, and network distribution software. Set operational specifications, and formulate and analyze software requirements. May design embedded systems software. We are seeking a DevOps Engineer to join our Data Science team and help build and maintain the infrastructure that powers machine learning solutions. In this role, you will design and implement robust DevOps practices tailored for ML Ops workflows, enabling seamless integration, deployment, and monitoring of machine learning models in production environments. You will work closely with data scientists and ML engineers to ensure scalable, secure, and efficient operations across the ML lifecycle.

Requirements

Bachelor’s degree in Computer Science, Engineering, or related field.
5 years of experience in DevOps or related engineering roles.
Master's degree and 3 years of relevant experience.
PhD and 0 years of experience.
NNS Apprentice School graduate
Navy Nuclear Power School (NNPS) graduate
Associate's Degree or other formal 2 year program and 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 and 4 years combined of Manufacturing, Shipbuilding, Trades, Military experience or other relevant exempt experience
High School/GED and 8 years combined of Manufacturing, Shipbuilding, Trades, Military experience or other relevant non-exempt experience
A relevant professional certification can be substituted for a Bachelor's Degree.
Strong proficiency with CI/CD tools (Jenkins, GitLab CI, Azure DevOps).
Hands-on experience with 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 tools (Prometheus, Grafana, ELK stack).
Solid understanding of version control systems (Git).

Nice To Haves

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).
Contributions to open-source DevOps or ML Ops projects.
Ability to work collaboratively in a fast-paced, cross-functional environment.

Responsibilities

Developing and maintaining CI/CD pipelines for ML applications.
Automating infrastructure provisioning and configuration using modern DevOps tools.
Implementing containerization and orchestration for ML workloads.
Ensuring system reliability, security, and compliance in ML Ops environments.
Collaborating with cross-functional teams to optimize deployment and monitoring processes.