Description
The company accelerates the development of next-generation aerospace and defense technologies through reusable hypersonic flight testing. Our autonomous, air-launched Talon-A vehicles provide access to operationally realistic flight environments at speeds exceeding Mach 5, delivering critical data that helps customers validate system performance and advance technologies toward deployment. Our team brings together expertise in engineering, manufacturing, and flight operations to solve complex challenges and advance capabilities essential to national security.
The AI Solutions Engineer contributes to the development, integration, and operation of AI capabilities across the company. The Enterprise Software Team integrates a mix of COTS and custom tools to provide the right data, functionality, and AI-enabled capabilities to the right user at the right time.
This position will focus on the infrastructure, integrations, observability, and developer tools required to operationalize AI for vehicle design, manufacture, flight test, and business teams. Responsibilities may include hosting and maintaining large language model (LLM) and Retrieval-Augmented Generation (RAG) systems; developing logging and monitoring capabilities; operating AI workloads in AWS; optimizing vector search systems such as Qdrant; and building APIs, plugins, SDKs, and command-line tools for internal users.
The AI Platform and MLOps Engineer will also evaluate and integrate COTS solutions, third-party services, open-source tools, and internally developed applications. Potential applications include meeting summarization and scheduling, CAD integrations, OpenWebUI and Hugging Face tools, team-specific plugins, and reusable software development kits. The scope of this position may be tailored to the selected candidate’s interests and experience, with particular opportunity for candidates experienced in ML workload optimization and cloud-based model operations.
ESSENTIAL RESPONSIBILITIES
Design, deploy, and operate reliable infrastructure for ML, LLM, and RAG workloads, with a focus on running and optimizing models in AWS and supporting future AI compute requirements.
Host, maintain, monitor, and optimize RAG systems, including Qdrant and other retrieval infrastructure, while developing the logging, observability, and alerting needed to assess system health, performance, cost, and reliability.
Serve as a subject matter expert for MLOps and AI workload operations, including model deployment, inference services, resource utilization, and production support.
Integrate and support COTS products, third-party services, open-source tools, and custom AI capabilities, sharing responsibility for their monitoring, security, cost, performance, and reliability.
Develop APIs, plugins, and reusable integrations that make AI capabilities available to the company teams, including tools for OpenWebUI, Hugging Face, Wilbur, and other applicable platforms.
Collaborate with internal stakeholders to develop team-specific AI integrations and workflows, and to deliver practical interfaces between AI services and existing applications, including frontend applications, Streamlit services, NGINX load balancers, and other web infrastructure.
Collaborate with software, data, infrastructure, security, and domain teams to evaluate AI technologies and deliver maintainable, user-focused solutions.