About the Role

We are seeking a Lead AI/ML Developer and Edge Systems Architect to design, develop, and operationalize advanced AI/ML models, agents, and decision‑support capabilities for Army commander workflows. This role is highly technical and hands‑on, supporting distributed operational environments, edge inference requirements, and multicloud integration across Oracle Cloud and AWS.

You will work closely with Army personnel and industry partners to architect scalable AI solutions, parameterize edge systems, and manage an edge emulation environment on OCI to support model testing and optimization.

Responsibilities

AI/ML Development and Architecture

• Build and deploy AI/ML models and automated decision workflows for cloud and edge environments.
• Create optimized, SWaP‑aware model variants for low‑size, low‑power edge systems.
• Develop algorithms that support real‑time inference under degraded, intermittent, or sparse data conditions.
• Implement MLOps pipelines for automated training, packaging, testing, and deployment.

Edge Systems and Emulation

• Lead parameterization of designated edge systems (TBD).
• Stand up and maintain an edge emulation environment in Oracle Cloud Infrastructure.
• Develop benchmarks to evaluate model performance under real‑world tactical conditions.
• Support automated tuning and profiling based on edge compute constraints.

Multicloud Integration

• Integrate AWS DMX and Model Garden into the approved multicloud architecture.
• Design and maintain an Oracle Database for storing edge system parameters.
• Enable secure data ingest, orchestration, and cross‑cloud interoperability across OCI, AWS, and Army environments.

Collaboration and Leadership

• Lead the Parameterization and Edge Emulation Working Group.
• Partner with government and industry stakeholders to align technical requirements.
• Provide mentorship to ML engineers, data engineers, and integrators.

Required Qualifications

• 8+ years in AI/ML development, distributed systems, or edge computing.
• Fluency in Python and modern deep learning frameworks (PyTorch, TensorFlow).
• Experience with MLOps, CI/CD, and automated model deployment pipelines.
• Background in multicloud environments (OCI, AWS, Google).
• Strong knowledge of SWaP‑constrained edge computing and model optimization (quantization, pruning, distillation).
• Experience working with edge or embedded systems simulations or emulations.
• Ability to collaborate with government personnel; clearance‑eligible.

Preferred Qualifications

• Experience with DoD missions, C5ISR programs, or tactical edge AI.
• Familiarity with Kubernetes, containerization, and cross‑cloud orchestration.
• Hands‑on experience with OCI, Oracle Autonomous Database, AWS DMX, and Model Garden.
• Understanding of multicloud security and cross‑domain workflows.

Success Indicators

• Delivery of scalable AI/ML models that perform reliably at cloud scale and edge environments.
• Successful operation of an OCI‑based emulation environment for testing and optimization.
• Effective collaboration across government and industry teams.
• Seamless integration of AWS DMX/Model Garden into the multicloud architecture.