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Job Description: 

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. Please consider applying if you meet the following qualifications.

Required Qualifications:

• 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.

Desired Qualifications:

• 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.