Software/Machine Learning Engineer - Agentic AI Systems - R139
Expedition Technology
- Location
- Onsite (Herndon, VA)
- Employment
- Full-time
- Level
- Mid Level
Posted 2 weeks ago
About the Role
Expedition Technology is seeking a Software/Machine Learning Engineer to build intelligent, agentic AI systems for mission-critical defense environments. The role involves designing scalable backend services and workflows that integrate machine learning models to solve complex real-world problems.
Skills
Python
Machine Learning
Software Engineering
Agentic AI
Cloud-native Architectures
AWS
Docker
Kubernetes
API Development
Data Pipelines
Retrieval-based Workflows
Linux
Git
Agile
Large Language Models
Distributed Systems
Full job details
Expedition Technology (EXP) is seeking a Software / Machine Learning Engineer who enjoys building intelligent systems that combine machine learning, automation, and modern software engineering. We’re looking for engineers who can develop scalable production software while integrating machine learning models, AI services, and automated workflows to solve challenging real-world problems.
This role focuses on building next-generation AI-enabled systems that orchestrate machine learning models, gather and analyze information from multiple sources, and deliver actionable results to users in mission environments. We hire people who excel at what they do and empower them to innovate with flexibility and autonomy.
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.
Qualifications 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.
- Comfortable working in Linux environments and using command-line tooling.
- Experience using Git-based development workflows and software engineering best practices.
- Ability to work independently while collaborating effectively within a multidisciplinary team.
- Strong desire to learn new technologies and solve challenging technical problems.
Preferred Qualifications:
- Experience with large language models (LLMs), agentic systems, or AI workflow orchestration.
- Experience with computer vision, object detection, or multi-modal machine learning systems.
- Experience with distributed systems, event-driven architectures, or message-based processing.
- Experience deploying ML applications in AWS environments.
- Experience supporting systems in classified or mission-critical environments.