AL/ML Engineer

Role overview

Responsibilities

• check_circle Develop and maintain automated data pipelines to ingest, parse, and integrate non-standard defense data, intelligence feeds, and weapon system profiles.
• check_circle Design, implement, and fine-tune large language models (LLMs) and agentic AI frameworks using modern programming languages such as Python and C++.
• check_circle Build and manage APIs and integration layers to connect disparate AI/ML services into unified simulation and analysis environments.
• check_circle Support the development of autonomous and semi-autonomous agent systems for decision support and simulation use cases.
• check_circle Advise on the architecture and implementation of secure sandboxed environments for testing, validation, and deployment of AI solutions.
• check_circle Collaborate with government and technical stakeholders to ensure AI systems meet operational, security, and performance requirements.

Basic qualifications

• Bachelor's with 12+ years (or commensurate experience)
• Clearance Required: None
• Demonstrated ability to design and implement automated data pipelines for complex, heterogeneous defense or intelligence datasets.
• Experience developing and fine-tuning large language models (LLMs) and building agent-based AI systems.
• Proficiency in programming languages such as Python and C++ for AI/ML development and system integration.
• Experience designing and implementing APIs for integration of AI/ML services and distributed systems.
• Knowledge of secure system design, including sandboxed environments for testing and validation of AI solutions.
• Bachelor’s degree in Computer Science or closely related field with a focus on artificial intelligence, machine learning, or software engineering.

Preferred qualifications

• Experience working in defense, intelligence, or government environments.
• Background in autonomous systems, reinforcement learning, or multi-agent systems.
• Experience integrating AI/ML models into simulation, wargaming, or Modeling & Simulation (M&S) environments.
• Familiarity with MLOps practices, model deployment, and scalable cloud-based AI infrastructure.
• Experience handling structured and unstructured data at scale in mission-critical environments.
• Strong understanding of AI safety, model evaluation, and validation in constrained or classified systems.