AI Software Engineer-Principal

At Net Sage, we are more than a government contractor—we are a mission-focused partner dedicated to advancing national security through exceptional cyber expertise. We believe our success begins with our people, which is why we invest in and care for our employees. We know that employee retention is mission continuity, and we are committed to building long-term careers where our people can grow alongside our customers' evolving missions.

Our employees enjoy meaningful work, competitive compensation, outstanding benefits, and opportunities to expand their skills across multiple mission areas. To learn more about our comprehensive benefits and what it's like to build your career at Net Sage, visit our Careers page.

We are seeking a Principal Artificial Intelligence (AI) Software Engineer to design and deliver advanced artificial intelligence and machine learning (AI/ML) solutions that enhance intelligence analysis and operational decision-making. In this role, you will lead the development and operationalization of advanced AI-enabled applications integrating machine learning, large language models (LLMs), and modern software engineering practices to automate complex workflows and deliver mission-focused capabilities.

The ideal candidate for this position will possess deep expertise in AI/ML technologies, software architecture, and cloud-native application development, with a proven ability to translate emerging technologies into scalable, operational solutions.

• TS/SCI with polygraph security clearance.
• A Bachelor of Science (B.S.) degree in Computer Science or a related field and eleven (11) years of relevant experience. Also acceptable are a Doctoral degree (PH.D.) and seven (7) years, a Master of Science (M.S.) degree and nine (9) years, or an Associate of Science (A.S.) degree and thirteen (13) years of relevant experience.
• Proficiency with object-oriented languages like Java and Python.
• Experience designing and developing AI/ML applications using modern software engineering methodologies.
• Experience working with LLMs and generative AI technologies.
• Experience with modern deep learning architectures, including deep neural networks (DNNs), recurrent neural networks (RNNs), and attention-based architectures.
• Experience with ML techniques, including classification, clustering, collaborative filtering, semantic search, and information retrieval.
• Experience using Jupyter Notebooks.

• Experience implementing retrieval‑augmented generation (RAG), Model Context Protocol (MCP), and AI agent frameworks.
• Experience designing autonomous or agentic AI systems.
• Strong understanding of statistical modeling, probability theory, Bayesian inference, covariance analysis, and machine learning fundamentals.