Software Engineer, AI Systems

Key Responsibilities

• Design, develop, deploy, and maintain AI-enabled software systems that leverage multimodal data sources to classify, assess, and model human performance and operational behaviors.
• Build and integrate AI capabilities—including LLMs, agentic systems, computer vision models, and multimodal analytics—into scalable software applications and services.
• Design and implement backend services, APIs, workflows, and software infrastructure that support production AI applications.
• Collaborate within agile, cross-functional teams to transition research algorithms into operational software systems.
• Develop, test, containerize, deploy, and maintain production-ready applications using modern software engineering and DevSecOps practices.
• Contribute to software architecture, system integration, testing, CI/CD pipelines, and cloud or edge deployment strategies.
• Successfully design and implement scientific and technical components of projects and proposals.
• Lead execution of technical tasks, ensuring work is delivered with high quality and on schedule.
• Contribute to technical reports, publications, presentations, proposals, and customer deliverables.
• Support customer engagements through software demonstrations, integration activities, technical discussions, and capability briefings.

Requirements

• 3-5 years of relevant experience and ability to obtain and maintain a US Government Security Clearance.
• Willingness to travel when necessary.
• Demonstrated technical ability and initiative to lead and execute work independently.
• Understanding across multiple technical domains, with expertise spanning AI/ML and software engineering.
• Ability to apply engineering principles and software development practices to produce high-quality, customer-focused technical solutions.
• Strong software engineering experience, including: Object-oriented programming (Python preferred; Java, C#, or similar languages a plus).
• Software architecture and modular design principles.
• API and backend service development.
• Version control and collaborative development workflows.
• Testing, debugging, and production software practices.
• Containerization and deployment technologies (Docker, Kubernetes, CI/CD pipelines).
• Experience building AI/ML systems using frameworks such as: PyTorch, TensorFlow, Hugging Face, LangChain, or similar tools.
• LLMs, NLP, agentic AI workflows, computer vision, or multimodal systems.
• Data processing pipelines and model development from large datasets.
• Experience deploying AI models into operational environments, cloud platforms, or edge systems preferred.
• Familiarity with MOSA, MBSE, and Government Reference Architectures, including modular design, defined interfaces, and standards-based integration is preferred.
• Familiarity with MLOps, cloud technologies (AWS, Azure, Kubernetes), and scalable deployment architectures is a plus.
• Experience translating research prototypes into maintainable, production-quality systems strongly preferred.