Today
Secret
Mid Level Career (5+ yrs experience)
$150,000 - $210,000
25%
IT - Data Science
Macdill AFB, FL (On-Site/Office)
Responsibilities include, but are not limited to:
-Operationalize ML models by building robust pipelines for training, evaluation, deployment, and monitoring across diverse compute environments (cloud, on-prem, and edge).
-Collaborate with development teams and mission stakeholders to translate requirements into ML systems that can be deployed and sustained in operational settings.
-Implement CI/CD practices for ML, enabling automated testing, packaging, and deployment of models and data pipelines.
-Manage ML infrastructure and tooling, including containerization (Docker), orchestration (Kubernetes), and model serving platforms (e.g., Seldon, KServe, BentoML).
-Develop monitoring and observability systems to track model performance, data drift, and resource utilization, ensuring reliability in mission environments.
-Contribute to security and compliance in ML pipelines, ensuring model deployments meet defense and customer requirements.
-Explore and integrate modern MLOps technologies to improve reproducibility, scalability, and maintainability of ML capabilities.
Necessary Skills and Experience:
-Bachelor’s degree in Computer Science, Electrical Engineering, Data Science, or a related technical discipline. Master’s degree preferred.
-5+ years of professional experience in software engineering, machine learning, or related fields.
-Experience with MLOps tools and frameworks (MLflow, Kubeflow, Airflow, DVC, etc.).
-Proficiency in building and deploying containerized ML services (Docker, Kubernetes).
-Strong understanding of CI/CD pipelines and DevOps practices applied to ML.
-Familiarity with deep learning frameworks (PyTorch, TensorFlow) and their deployment requirements.
-Knowledge of monitoring and logging systems (Prometheus, Grafana, ELK/EFK stacks).
-Strong software engineering background (Python required; C, Rust, or MATLAB a plus).
-Active U.S. Government Secret clearance with SCI eligibility (TS/SCI).
Beneficial Skills and Experience:
-Experience in DoD programs and drone (UAS) development.
-Experience working with diverse data types (RF signals, imagery, video, sensor feeds) is a plus.
-Experience deploying ML models to edge or constrained environments is highly desirable.
-Familiarity with secure software deployment in defense environments.
-Experience with air-gapped registries, offline updates, reproducible builds, and SBOM attestation in CI.
-Experience with Explainable AI/ML.
-Operationalize ML models by building robust pipelines for training, evaluation, deployment, and monitoring across diverse compute environments (cloud, on-prem, and edge).
-Collaborate with development teams and mission stakeholders to translate requirements into ML systems that can be deployed and sustained in operational settings.
-Implement CI/CD practices for ML, enabling automated testing, packaging, and deployment of models and data pipelines.
-Manage ML infrastructure and tooling, including containerization (Docker), orchestration (Kubernetes), and model serving platforms (e.g., Seldon, KServe, BentoML).
-Develop monitoring and observability systems to track model performance, data drift, and resource utilization, ensuring reliability in mission environments.
-Contribute to security and compliance in ML pipelines, ensuring model deployments meet defense and customer requirements.
-Explore and integrate modern MLOps technologies to improve reproducibility, scalability, and maintainability of ML capabilities.
Necessary Skills and Experience:
-Bachelor’s degree in Computer Science, Electrical Engineering, Data Science, or a related technical discipline. Master’s degree preferred.
-5+ years of professional experience in software engineering, machine learning, or related fields.
-Experience with MLOps tools and frameworks (MLflow, Kubeflow, Airflow, DVC, etc.).
-Proficiency in building and deploying containerized ML services (Docker, Kubernetes).
-Strong understanding of CI/CD pipelines and DevOps practices applied to ML.
-Familiarity with deep learning frameworks (PyTorch, TensorFlow) and their deployment requirements.
-Knowledge of monitoring and logging systems (Prometheus, Grafana, ELK/EFK stacks).
-Strong software engineering background (Python required; C, Rust, or MATLAB a plus).
-Active U.S. Government Secret clearance with SCI eligibility (TS/SCI).
Beneficial Skills and Experience:
-Experience in DoD programs and drone (UAS) development.
-Experience working with diverse data types (RF signals, imagery, video, sensor feeds) is a plus.
-Experience deploying ML models to edge or constrained environments is highly desirable.
-Familiarity with secure software deployment in defense environments.
-Experience with air-gapped registries, offline updates, reproducible builds, and SBOM attestation in CI.
-Experience with Explainable AI/ML.
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