Principal Platform Engineer (MLOps)

Clarity Innovations is a trusted national security partner, dedicated to safeguarding our nation's interests and delivering innovative solutions that empower the Intelligence Community (IC) and Department of Defense (DoD) to transform data into actionable intelligence, ensuring mission success in an evolving world.

Our mission-first software and data engineering platform modernizes data operations, utilizing advanced workflows, CI/CD, and secure DevSecOps practices. We focus on challenges in Information Warfare, Cyber Operations, Operational Security, and Data Structuring, enabling end-to-end solutions that drive operational impact.

We are committed to delivering cutting-edge tools and capabilities that address the most complex national security challenges, empowering our partners to stay ahead of emerging threats and ensuring the success of their critical missions. At Clarity, we are people-focused and set on being a destination employer for top talent, offering an environment where innovation thrives, careers grow, and individuals are valued. Join us as we continue to lead innovation and tackle the most pressing challenges in national security.

Overview

This person will focus on project platform pipelines and enabling MLOps to design, implement, and maintain automated pipelines supporting the full machine learning lifecycle within the JDE and JCWA enterprise. The ideal candidate will have hands-on experience building and orchestrating MLOps pipelines, including data ingestion and preprocessing, model training, fine-tuning and validation, automated deployment, and continuous monitoring and retraining. This person will implement CI/CD systems and model registries to ensure scalable, repeatable, and reliable ML workflows. Familiarity with various MLOps pipeline types - such as CI/CD, continuous training (CT) pipelines, and automated model deployment and monitoring pipelines - is essential for this role.

Technical Background Requirements

• Proficiency in at least one major cloud provider (AWS, Azure, Google Cloud Platform), including deploying, managing, and scaling cloud infrastructure and services
• Expertise with automation and configuration management tools such as Terraform, Ansible, Chef, or Puppet for provisioning and managing infrastructure.
• Advanced knowledge of continuous integration and deployment tools (Jenkins, GitLab CI, CircleCI, etc.) to automate software and model delivery workflows
• Strong experience with Docker and Kubernetes
• Proficiency in scripting languages such as Python, Bash, or PowerShell
• Familiarity with monitoring and logging tools (Prometheus, ELK Stack, Grafana, Splunk)
• Solid understanding of security best practices across the software delivery lifecycle
• Experience building and maintaining MLOps pipelines that manage the full ML model lifecycle (e.g., versioning, training, validation, deployment, monitoring, and retraining)
• Ability to work closely with engineers to optimize model deployment, monitor model performance, and address ML-specific operational challenges (e.g., data drift, model retraining triggers)
• Knowledge of model serving frameworks (TensorFlow Serving, TorchServe, Seldon Core, etc.)
• Automation of ML data pipelines, feature engineering, and model retraining using tools like Kubeflow, MLflow, or Airflow
• Experience with cloud-native MLOps tools and managed services (e.g., AWS SageMaker, Azure ML, Google Cloud Platform Vertex AI) is highly desirable

Additional Requirements

• TS/SCI w/CI Poly
• Available to travel onsite 3 days per week at a minimum

We are an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.