Senior AI/ML Engineer

Key Responsibilities

Lead CBM+ AI/ML initiatives from concept through production deployment, ensuring technical solutions align with mission outcomes, business objectives, and enterprise architecture standards.
Architect and oversee scalable data pipelines, including data ingestion, ETL/ELT processing, feature engineering, data validation, and sustainment of production data workflows.
Design, develop, and optimize machine learning, deep learning, and generative AI models to solve complex operational problems and improve predictive maintenance capabilities.
Perform advanced exploratory data analysis, statistical modeling, and data visualization to uncover trends, correlations, risk indicators, and actionable insights that guide product and mission decisions.
Build, maintain, and extend knowledge graph capabilities to support semantic reasoning, data integration, and AI-enabled decision support.
Provide technical leadership to multi-functional engineering teams, mentoring junior engineers and guiding best practices for model development, MLOps, testing, documentation, and secure deployment.
Collaborate with software, data, cloud, and product teams to integrate trained models into production applications, APIs, and operational workflows.
Establish monitoring strategies for data quality, model performance, drift, bias, and degradation, and lead mitigation efforts to maintain reliable AI/ML system performance.

Required Experience/Clearance

An active Secret security clearance is mandatory for this position. Applicants must currently possess an active Secret clearance to be considered. Candidates without an active clearance will not be considered.
Education & Experience: Master’s degree in a STEM (Science, Technology, Engineering, or Mathematics) field or proven equivalent professional experience.
Natural Language Processing (NLP): Minimum of 5 years of experience incorporating applied NLP, data labeling, and entity or keyword extraction.
Analytics & Data Engineering: Proven proficiency with Databricks, PySpark, SQL, data governance frameworks, and technical documentation standards.
Statistical Foundations: Deep understanding and practical application of statistical distributions for data assessment, data analysis, and predictive modeling.
Visualization & Data Products: Experience developing dashboard or data end-products utilizing tools such as Streamlit, Qlik Sense, or similar platforms.
Tools & Methodologies: Experience using project management and productivity platforms including Jira, Confluence, Lucidchart, and Microsoft Office Suite (Word, Excel, PowerPoint).
Communication & Initiative: Demonstrated self-starter with strong interpersonal skills, with the proven ability to translate complex technical findings for executive and non-technical stakeholders.
Travel: Ability to travel minimally as needed to engage with government customers and project stakeholders.

Desired Experience

We encourage all candidates who meet the basic requirements to apply, even if you do not have any of the following experience:

• Ph.D. in a STEM field, with a preference for Data Science, Data Analytics, or Computer Science.
• Advanced expertise applying artificial intelligence and machine learning (AI/ML) to predictive maintenance, condition-based maintenance, or defense logistics challenges.
• Direct experience developing sensor-based failure prediction models and equipment health indicators.
• Practical experience conducting Monte Carlo simulations and calculating statistical confidence intervals.
• Demonstrated expertise in end-to-end data pipeline engineering, including customer-facing UI/UX integration.
• Prior experience supporting defense aviation data environments or DoD enterprise systems.