Software Engineer I - Inference with Security Clearance

Black Eagle Defense; Fort Meade (MD); On-site; USD 124,000 - 181,000; Full time

Job summary

Black Eagle Defense in Fort Meade, Maryland seeks a Software Engineer I - Inference to join our AI infrastructure team. You will help build and maintain the foundation for customer AI capabilities and ensure access to high-quality LLMs across the inference stack.

The role emphasizes collaborating with model vendors, deploying containerized services with Kubernetes and CI/CD, and operating in AWS. A bachelor’s degree with 3 years SWE experience is required, with potential substitution for more

Qualifications

• Three (3) years of SWE experience in programs and contracts of similar scope, type, and complexity is required.
• A Bachelor's degree in Computer Science or a related discipline from an accredited college or university is required.
• Four (4) years of additional SWE experience on projects with similar software processes may be substituted for a bachelor's degree.

Responsibilities

• Procure, configure, and test new inference models to prepare them for seamless release to the user base.
• Develop in-house services and techniques to guarantee continuous, high-quality inference performance.
• Partner with model vendor teams to establish reliable integration pipelines for closed-source models.
• Collaborate with teammates on surge efforts to address high-priority, short-term customer inference demands.
• Engage with cross-functional teams to build resilient service infrastructure and integrate LLM-powered tools for end-user needs.

Skills

Python development

Strong collaboration and communication

Learning new technologies

Problem-solving in dynamic environment

Education

Bachelor's degree in Computer Science or related field

Tools

Argo CD CI/CD

Kubernetes

Docker

AWS

Job description

Job Description SALARY RANGE $124,000 - $181,000/year. DUTIES As a successful candidate for the Software Engineer I - Inference role, you will join our AI infrastructure team to build and maintain the foundation for customer AI capabilities while supporting a broader ecosystem of AI-enabled applications. Your primary focus will be ensuring seamless access to the highest quality large language models (LLMs) throughout the inference software stack. Operating in a dynamic environment where requirements shift as mission needs evolve and new technologies emerge, you will continuously sharpen your skills and turn loosely defined problems into impactful, working solutions. Required Skills SKILLS * Procure, configure, and test new inference models to prepare them for seamless release to the user base

• Develop in-house services and techniques to guarantee continuous, high-quality inference performance
• Partner with model vendor teams to establish reliable integration pipelines for closed-source models
• Collaborate with teammates on surge efforts to address high-priority, short-term customer inference demands
• Engage with cross-functional teams to build resilient service infrastructure and integrate LLM-powered tools for end-user needs QUALIFICATIONS Three (3) years' experience as a SWE in programs and contracts of similar scope, type, and complexity is required. A Bachelor's degree in Computer Science or a related discipline from an accredited college or university is required. Four (4) years of additional SWE experience on projects with similar software processes may be substituted for a bachelor's degree. Additional Requirements: * Develop with Python and other modern programming languages
• Utilize Argo CD or other modern CI/CD frameworks
• Deploy and manage containerized applications using Kubernetes and Helm
• Operate within AWS or other major cloud service provider environments
• Demonstrate a proven ability to rapidly learn and adopt emerging technologies
• Communicate clearly and collaborate proactively to address technical and mission needs Desired Skills NICE-TO-HAVES * Leverage vLLM, LiteLLM, or similar inference-serving frameworks
• Apply modern LLM hosting frameworks and deployment practices
• Support production software using Site Reliability Engineering (SRE) best practices
• Utilize Elastic, Grafana, Prometheus, or other observability frameworks
• Package and manage applications using Docker and containerization
• Implement traffic shaping and quality-of-service engineering strategies
• Demonstrate strong interest and foundational knowledge in hosting AI capabilities