Software/Machine Learning Engineer - Agentic AI Systems - R139

As a member of our team, you will:

• Work with a talented, collaborative team in an Agile environment to develop new AI-enabled capabilities.
• Design and develop software systems that integrate machine learning models into operational workflows.
• Build agentic workflows that can automatically perform inference, gather additional information, query tools and services, and disseminate results.
• Develop and maintain scalable backend services using Python, containerized technologies, and cloud-native architectures.
• Collaborate with engineers and researchers to deploy and operationalize machine learning capabilities.
• Design and implement APIs and data pipelines that support model execution, information retrieval, and downstream consumers.
• Develop retrieval-based and knowledge-enhanced workflows to support difficult reasoning and analysis tasks.
• Contribute to SDLC best practices, automated testing, and software quality initiatives.
• Identify and resolve performance, scalability, and reliability bottlenecks across the system.
• Help shape the architecture of next-generation AI and machine learning systems.

we're seeking:

• U.S. Citizenship; ability to obtain and maintain a U.S. Government security clearance.
• Active Secret clearance at minimum. Active TS/SCI security clearance strongly preferred.
• BA/BS or higher in Computer Science, Computer Engineering, Data Science, Applied Mathematics, or a closely related field.
• 3+ years of experience developing production software using Python.
• Experience designing and implementing backend services and APIs.
• Experience building, deploying, integrating, or supporting machine learning systems.
• Familiarity with cloud-native environments and AWS services.
• Experience with Docker, Kubernetes, or other containerized deployment technologies.
• Understanding of machine learning workflows, model deployment, and inference pipelines.
• Familiarity with retrieval systems, knowledge-based systems, search technologies, or RAG architectures.
• Experience integrating multiple services, APIs, or models into end-to-end workflows.