Senior Applied AI Engineer III - Data Science & Analytics

• Design and develop AI-enabled analytics applications using Python and modern software engineering practices
• Perform exploratory data analysis, feature engineering, statistical analysis, and machine learning on complex mission datasets
• Develop pattern-of-life, anomaly detection, clustering, and predictive analytics capabilities
• Build Retrieval-Augmented Generation (RAG) and LLM-powered workflows to enhance analytical processes
• Collaborate directly with mission customers to understand datasets and develop tailored, AI-driven analytical solutions
• Integrate structured and unstructured data sources into scalable AI applications
• Evaluate emerging AI and machine learning techniques for mission applicability
• Mentor junior engineers and contribute to overall technical direction across the team

QUALIFICATIONS Twelve (12) years’ experience as an SWE in programs and contracts of similar scope, type, and complexity is required. A Bachelor’s degree in Computer Science or a related discipline from an accredited college or university is required. Four (4) years of additional SWE experience on projects with similar software processes may be substituted for a bachelor’s degree.

Additional requirements:

• Proven experience developing production-grade software using Python
• Demonstrated background in data science, machine learning, or statistical analysis
• Proficiency with core data science libraries such as Pandas, NumPy, Scikit-learn, or similar tools
• Hands-on experience building functional AI or machine learning applications
• Strong analytical reasoning and technical problem-solving skills
• Experience working with SQL, Elasticsearch, or other large-scale data platforms
• Strong communication skills with the ability to convey technical concepts to both technical and non-technical stakeholders
• Track record of collaborating directly with customers to solve analytical challenges, * Experience with LLMs, prompt engineering, or Retrieval-Augmented Generation (RAG) architectures
• Experience with geospatial analysis, pattern-of-life analytics, or time-series analysis
• Experience with data visualization tools and interactive dashboard development
• Experience deploying machine learning models into production environments
• Experience supporting classified mission environments