Position Details
About this role
This role involves building and operationalizing AI/ML systems in mission environments, supporting model deployment, data pipelines, and reproducibility standards within secure, classified settings.
Key Responsibilities
- Build ML pipelines
- Deploy models into mission environments
- Manage ML workflows (Kubeflow, Airflow)
- Ensure reproducibility and model monitoring
- Support data governance
Technical Overview
The technical environment includes Kubernetes, Docker, Terraform, Ansible, Kubeflow, Airflow, MLflow, and monitoring tools like Prometheus and Grafana. Focus is on deploying scalable, reliable ML workflows.
Ideal Candidate
The ideal candidate is a mid-level MLOps engineer with experience deploying ML systems in mission-critical environments, proficient in Kubernetes, Docker, and infrastructure as code tools like Terraform and Ansible. Strong understanding of ML workflows and model management is essential.
Must-Have Skills
Nice-to-Have Skills
Tools & Platforms
Required Skills
Hard Skills
Soft Skills
Industry & Role
Clearance & Visa
Keywords for Your Resume
Deal Breakers
Lack of experience with Kubernetes or Docker, No experience deploying ML models, No experience with ML pipelines or workflows, Lack of security clearance or experience in classified environments
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