Machine learning engineer

Job Description

Insight Global's client is looking for a secret cleared ML engineer onsite at MacDill AFB who will be a key technical contributor in advancing artificial intelligence and machine learning capabilities.We're seeking a Machine Learning Engineer with deep expertise in MLOps, model deployment, and infrastructure automation to build scalable, secure, and production-grade ML systems. The successful candidate will be passionate about building and automating ML pipelines, implementing modern MLOps practices, and driving innovation that directly impacts mission outcomes

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [email protected] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:

Skills and Requirements

Bachelor's degree in Computer Science, Electrical Engineering, Data Science, or a related technical discipline. (Master's 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 PyTorch, TensorFlow, and deployment best practices.

Knowledge of monitoring and logging systems (Prometheus, Grafana, ELK/EFK stacks).

Proficiency in Python (C, Rust, or MATLAB a plus).

Secret clearance Prior work on DoD programs, UAS/drone systems, or defense AI applications.

Experience working with diverse data types (RF signals, imagery, video, sensor feeds).

Experience deploying ML models to edge or constrained environments.

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.