AI/ML Engineer (Tactical Networks - CANES) | Active Secret clearance

Overview

Transform data into decisive advantage as a AI/ML Engineer with GDIT. A career in applied machine learning at GDIT means building mission-grade analytics and automation that harden and optimize Navy tactical networks. You’ll be at the forefront of innovation—advancing Consolidated Afloat Networks and Enterprise Services (CANES) and related platforms with NIWC Pacific (Codes 55131/55132/55133) in support of PMW-160 Tactical Networks.

At GDIT, people are our differentiator. Our work depends on a Senior AI/ML Engineer who can design, deploy, and sustain models at the edge—within virtualized, bandwidth-constrained, and intermittently connected environments—while meeting rigorous cybersecurity and programmatic standards.

MEANINGFUL WORK AND PERSONAL IMPACT

• Design and implement ML solutions for network assurance and cyber defense: anomaly detection, fault prediction, QoS optimization, capacity planning, and automated triage across ashore/afloat/subsurface/airborne domains.
• Build secure data pipelines (ETL/ELT) from CANES telemetry, logs, and performance counters; develop features for time-series, graph, and NLP use cases; enforce data governance and labeling quality.
• Operationalize models with MLOps: containerize (Docker), orchestrate (Kubernetes/Openshift), version (MLflow/DVC), and automate CI/CD (GitLab/Jenkins) for model build/test/deploy in disconnected and low-bandwidth scenarios.
• Optimize for edge inference using ONNX/TensorRT and CPU/GPU acceleration; implement drift monitoring, retraining triggers, A/B or canary deployments, and model explainability for operator trust.
• Integrate analytics with enterprise tooling (e.g., REST/gRPC services, message buses, dashboards such as Grafana/Splunk) and with program configuration management (CMPro) and issue workflows (Jira/Confluence).
• Align solutions to Risk Management Framework (RMF) and DISA STIGs; produce artifacts (e.g., security design, data flows, test plans) supporting IATT/ATO packages in Enterprise Mission Assurance Support Service (eMASS) in coordination with IA teams.
• Support Developmental Test & Evaluation (DT&E), Test Readiness Reviews (TRRs), and lab events in NIWC Pacific facilities; collect test data, analyze results, and author technical reports/white papers.
• Mentor engineers and analysts on ML best practices; contribute to Systems Engineering Plans (SEPs) and DoD Architecture Framework (DoDAF) views related to data/analytics services.

What you’ll need to succeed (Required)

• Security Clearance: Active Secret clearance.
• Experience:
• 2+ years applying AI/ML to large-scale or mission systems (time-series/graph/NLP), including production deployments and lifecycle sustainment.
• 2+ years MLOps (containerization, orchestration, CI/CD, model/version management, monitoring) and secure software practices.
• Hands-on data engineering with SQL/NoSQL, stream processing (e.g., Kafka), and Python-based ML stacks (PyTorch or TensorFlow, scikit-learn, pandas).
• Demonstrated delivery in constrained/edge environments (performance tuning, model compression/quantization, resilience to disconnection).
• Familiarity with DoD cybersecurity processes (RMF, STIGs) and documentation practices.
• Education: BS in Computer Science, Electrical/Computer Engineering, Data Science, Applied Mathematics, or related field (equivalent experience may substitute per GDIT policy).
• Position Availability: Position is currently pending final contract award.
• Work Location: Onsite San Diego, CA (NIWC Pacific); ~10–25% CONUS/OCONUS travel to labs, shipyards, and fleet concentration areas per tasking.
• US Citizenship Required

What we’d love for you to have (Desired)

• Prior support to PMW-160/NIWC Pacific or CANES-like programs; understanding of Navy network operations, GPON, QoS, and transport/optical basics.
• Certifications: Security+ CE (or higher 8140 baseline if privileged access assigned), CKA/CKAD, AWS/Azure/GCP ML Specialty, Splunk Core/Enterprise.
• Experience with graph ML (PyG/NetworkX), time-series platforms (Kats, Prophet), and XAI (SHAP/LIME).
• Integration with observability stacks (Prometheus/Grafana), IaC (Ansible/Terraform), and secure SBOM/supply-chain controls.
• Participation in CCRI prep, DT/OT data analysis, and performance test harness development.

GDIT is your place

At GDIT, the mission is our purpose, and our people are at the center of everything we do.

• Growth: AI-powered career tool that identifies career steps and learning opportunities
• Support: An internal mobility team focused on helping you achieve your career goals
• Rewards: Comprehensive benefits and wellness packages, 401K with company match, and competitive pay and paid time off
• Flexibility: Full-flex work week to own your priorities at work and at home
• Community: Award-winning culture of innovation and a military-friendly workplace

Own your opportunity

Explore a career in data science and engineering at GDIT and you’ll find endless opportunities to grow alongside colleagues who share your determination for solving complex data challenges.