Machine Learning / Software EngineerVector Resources Inc • Albuquerque, New Mexico, United States

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Machine Learning / Software Engineer

Vector Resources Inc

Albuquerque, New Mexico, United States

Albuquerque, New Mexico, United States

About

Summary The Machine Learning / Software Engineer will lead technical modernization efforts across AI/ML automation, digital engineering transformation, and software development for the NNSA weapons complex. This role combines building production ML systems and AI-powered applications with advancing the enterprise toward digital thread, digital twin, and model-based approaches. The position requires a tech-forward engineer who can develop LLM-integrated solutions for process automation, architect data integration frameworks for digital engineering initiatives, lead software development teams building predictive models with user interfaces, and establish technical strategies that position the team as leaders in enterprise digital transformation. Work spans three key domains: (1) AI/ML automation including LLM integration for enterprise taxonomy standardization, predictive modeling systems, and process automation; (2) Digital engineering leadership to advance digital thread, digital twin, and model-based systems engineering capabilities; (3) Full-stack software development leading teams building applications with modern interfaces and data integration. This position offers the opportunity to shape how a large, complex enterprise modernizes its technical capabilities across weapons acquisition, sustainment, and logistics programs. Specific duties include: AI/ML Build and deploy AI/ML systems using LLMs and NLP to automate enterprise processes, including developing solutions to standardize part taxonomies across hundreds of thousands of components from disparate sites by intelligently mapping unique naming conventions to common UNSPSC codes. Develop full-stack applications integrating predictive models with user-friendly interfaces, including leading development of transportation management systems that transform complex inputs into actionable predictions and building turnkey solutions that teams can deploy across the enterprise. Build knowledge graphs and semantic data models to enable requirements traceability, system understanding, and intelligent querying across complex weapon system documentation, leveraging graph databases and ontologies to create a queryable digital thread. Digital Engineering Lead digital engineering transformation initiatives to advance the enterprise toward digital thread, digital twin, and model-based systems engineering, including architecting data integration frameworks that connect design, simulation, test, and manufacturing systems across the weapons complex lifecycle. Design and implement real-time data integration pipelines connecting sensors, IoT devices, simulation outputs, and enterprise systems to enable digital twin capabilities and predictive analytics for asset monitoring and lifecycle management. Software Development and Program Integration Lead and mentor software development teams, establishing technical standards, MLOps practices, and development workflows while directing the implementation of front-end interfaces, APIs, and cloud-based deployments. Identify requirements, interfaces, conflicts, and integration issues and provide recommended resolutions based on sound engineering rationale supported by thorough and comprehensive analysis. Assist DP to develop, implement, manage and maintain a configuration management process for logistics, including development and management of the technical tools for configuration management. Develop and implement business processes and operations for logistics and supply chain management. Analyze existing requirements processes and tools for effective implementation. Skills / Qualifications Experience building and deploying production machine learning systems and AI-powered applications, including NLP/LLM integration, predictive modeling, and full-stack development from data pipelines through user interfaces. Enterprise systems integration experience including connecting disparate data sources, building data integration frameworks for digital thread/digital twin applications, and knowledge of semantic data modeling, ontologies, or graph databases. Experience leading technical teams, mentoring developers, and establishing best practices for software development, including agile methodologies, CI/CD, and DevOps/MLOps workflows. Proficiency with ML/AI frameworks (PyTorch, TensorFlow, scikit-learn), LLM deployment, cloud platforms (AWS, Azure, GCP), and modern development tools including containerization (Docker, Kubernetes) and streaming data platforms (Kafka, Spark). Strong programming foundation in Python and experience with full-stack development (React, Vue, or similar frameworks); exposure to Model-Based Systems Engineering (MBSE) tools like Cameo Systems Modeler or digital twin platforms is highly valued along with proficiency in R, SQL, JavaScript, etc.; experience with IoT/sensor integration, real-time data streaming, or PLM system integration is a plus. Experience / Educational Requirements Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Software Engineering, Electrical Engineering, or related technical field with strong computational focus. (preferred) Other Unique Requirements Experience building production ML/AI systems that solve real business problems; exposure to digital engineering concepts (digital thread, digital twin, MBSE) or PLM/systems integration; demonstrated ability to lead technical modernization initiatives and introduce emerging technologies into large organizations. (preferred) Department of Energy (DOE) 6.X and/or DoD 5000-series acquisition experience. (preferred) Knowledge of the interfaces between DOE/NNSA programs, field sites, contractors, and other government agencies involved in weapons production, handling, and transportation. (preferred) Knowledge of DOE/NNSA weapons programs. (preferred)
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Albuquerque, New Mexico, United States

Languages

English

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