AI Engineer II

Kansas City National Security Campus•Kansas City, MO

18d•Onsite

About The Position

The Kansas City National Security Campus (KCNSC), managed and operated by Honeywell Federal Manufacturing & Technologies, is a premier advanced manufacturing facility that supports the safety, security, reliability and effectiveness of our nation's nuclear deterrent. With nearly 7,000 employees combined in Kansas City, Missouri, and Albuquerque, New Mexico, KCNSC protects our nation and allies by producing trusted national security products and services for the U.S. Department of Energy's National Nuclear Security Administration. We create innovative solutions to complex national security challenges, and lead with accountability, collaboration and an unwavering dedication to our mission and core values. This role contributes to the end-to-end design, development, productionization, and ongoing support of advanced artificial intelligence solutions for manufacturing or business applications, ensuring models are scalable, reliable, and seamlessly integrated into frontend and backend systems.

Requirements

Bachelors degree in quantitative field
At least two years relevant experience in computer science, data science, or related technical activities
Ability to travel as determined by the needs of the business
Ability to work on-site as directed by management and as determined by the needs of the business
Regular and reliable attendance is an essential function of this job
United States Citizenship
Ability to obtain and maintain, if required for position, a U.S. Department of Energy (DOE) security clearance (some positions require additional DOE designations)

Nice To Haves

Ability to grasp complex technical and business problems, prioritize tasks, and devise innovative, production ready AI solutions that align with manufacturing or enterprise objectives.
Demonstrated software engineering discipline: version control, unit/integration testing, continuous integration-continuous deployment (CI/CD), and documentation of code and system architecture.
Strong communication skills (verbal, written, presentation) to convey technical concepts to stakeholders, collaborate with cross-functional teams, and produce clear technical specifications and run books.
Deep knowledge of machine learning and deep learning concepts, including model design, training, evaluation, and optimization for performance, latency, and resource utilization.
Familiarity with distributed data processing frameworks and big data ecosystems (e.g., Spark, Kafka) to support high throughput training and real time inference pipelines.
Proven ability to create actionable visualizations or dashboards that surface model performance, data quality, and operational metrics for end users and decision makers.
Experience with API development, microservices architecture, and version control systems (Git).
Hands-on experience deploying AI applications on cloud platforms such as Azure or AWS.
Solid understanding of Large Language Models (LLMs), Natural Language Processing (NLP), and Retrieval-Augmented Generation (RAG).

Responsibilities

Develop AI/ML models and algorithms using disciplined software-engineering practices, including version control, automated testing, and thorough documentation.
Contribute to designing, building, and maintaining production-grade pipelines for model training, validation, deployment, and monitoring, applying CI/CD and modern orchestration and deployment practices.
Optimize model performance for latency, throughput, and resource utilization to meet real-time manufacturing or business requirements.
Collaborate with multiple stakeholders to translate business needs into AI system specifications, define service-level objectives, and provide technical guidance to data science partners.