SENIOR ML OPS SOFTWARE ENGINEER

Huntington Ingalls•Newport News, VA

8h•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 looking for a Senior ML Ops Engineer to join our Data Science team and lead the operationalization of machine learning models at scale. In this role, you will design and implement robust ML Ops frameworks that enable seamless deployment, monitoring, and lifecycle management of machine learning solutions. You will collaborate closely with data scientists and software engineers to ensure models are production-ready, reliable, and continuously optimized.Key responsibilities include:• Building and maintaining ML Ops infrastructure for model deployment and monitoring.• Developing automated workflows for training, testing, and CI/CD of ML models.• Implementing best practices for reproducibility, scalability, and governance in ML pipelines.• Managing model versioning, performance tracking, and retraining strategies.• Driving innovation in ML Ops processes to improve efficiency and reliability.

Requirements

Bachelor's degree in Computer Science, Engineering, or related field.
9 years of experience in software engineering, Master's degree and 7 years of relevant experience. PhD and 4 years of experience.
Strong proficiency in Python and experience with ML frameworks (TensorFlow, PyTorch).
Hands-on experience with ML Ops tools (MLflow, Kubeflow, Airflow) and cloud platforms (AWS, Azure, GCP).
Expertise in containerization and orchestration (Docker, Kubernetes).
Experience with CI/CD pipelines and version control systems (Git).
Solid understanding of monitoring, logging, and alerting for ML systems.

Nice To Haves

Master’s degree in Computer Science, Machine Learning, or related discipline with a focus on backend or systems development.
Experience in ML Ops, DevOps, or machine learning engineering.
Experience with feature stores, model registries, and automated retraining workflows.
Familiarity with distributed systems and large-scale data processing (Spark, Hadoop).
Knowledge of security, compliance, and governance in ML deployments.
Contributions to open-source ML Ops or DevOps projects.
Strong problem-solving skills and ability to work in cross-functional teams.

Responsibilities

Building and maintaining ML Ops infrastructure for model deployment and monitoring.
Developing automated workflows for training, testing, and CI/CD of ML models.
Implementing best practices for reproducibility, scalability, and governance in ML pipelines.
Managing model versioning, performance tracking, and retraining strategies.
Driving innovation in ML Ops processes to improve efficiency and reliability.