Software Engineer II - AI Infrastructure with Security Clearance

Black Eagle Defense; Fort Meade (MD); On-site; USD 143,000 - 200,000; Full time

Job summary

Black Eagle Defense in Fort Meade, MD seeks a Software Engineer II - AI Infrastructure to design, build, and operate scalable AI inference infrastructure for mission-critical services across the enterprise.

You will work on AI model serving, RAG, autonomous agents, and cloud-native platforms, emphasizing IaC, Kubernetes on AWS, Python automation, and observability with OpenTelemetry, Prometheus, and Grafana. Mentorship and cross-team collaboration are expected.

Qualifications

• Eight years of SWE experience in similar scope required.
• Bachelor's degree in Computer Science or related discipline required.

Responsibilities

• Design, implement, and optimize AI inference infrastructure at scale.
• Develop, deploy, and maintain production AI services including RAG and autonomous agents.
• Analyze requirements and define scalable, maintainable solutions for complex systems.
• Drive adoption of modern technologies, standards, and best practices.
• Implement monitoring, logging, and observability to improve platform performance.
• Automate provisioning, deployment, and configuration using IaC.

Skills

AI infrastructure

Python

AWS

Kubernetes

Observability

IaC

OpenTelemetry

Prometheus

Education

Bachelor's degree in Computer Science or related field

Tools

Grafana

Job description

Job Description SALARY RANGE $143,000 - $200,000/year DUTIES As a successful candidate for the Software Engineer II - AI Infrastructure role, you will support the development, operation, and evolution of the next generation of AI infrastructure that enables innovation across the customer organization. As part of a full-stack engineering team, you will design, implement, and maintain scalable platform capabilities that serve as the foundation for AI-powered applications and services. Your efforts will focus on AI inference infrastructure while supporting a broader ecosystem that includes advanced analytics, retrieval-augmented generation (RAG), autonomous agents, and emerging AI technologies. In this role, you will independently design, develop, deploy, and optimize infrastructure components that deliver reliable, secure, and high-performance AI capabilities at scale. You will collaborate with engineers, platform teams, and stakeholders to enhance platform reliability, drive adoption of modern technologies and engineering practices, and ensure AI services remain scalable, observable, and operationally resilient. Through cloud engineering, automation, systems integration, and platform development, you will help deliver the infrastructure that powers mission-critical AI solutions across the enterprise.

Required Skills

* Design, implement, and optimize infrastructure supporting AI model inference at scale

• Develop, deploy, and maintain production AI services and applications, including retrieval-augmented generation (RAG), autonomous agents, and emerging AI technologies
• Analyze ambiguous requirements and define scalable, maintainable solutions for complex systems and operational challenges
• Drive the adoption of modern technologies, engineering standards, and best practices across development teams
• Implement monitoring, logging, and observability capabilities to improve visibility into AI platform performance and reliability
• Automate infrastructure provisioning, deployment, and configuration management using Infrastructure-as-Code principles
• Ensure the availability, reliability, scalability, and performance of AI platform components and supporting services
• Contribute to the implementation of security best practices for AI systems, services, and data environments
• Design and integrate platform capabilities that support enterprise AI initiatives and operational requirements
• Collaborate with engineers, platform teams, and stakeholders to improve AI infrastructure and service delivery
• Troubleshoot complex infrastructure, platform, and application issues within production environments
• Provide technical guidance, knowledge sharing, and informal mentorship to junior engineers
• Support the continuous improvement and modernization of AI infrastructure, cloud environments, and platform operations
• Contribute to the full lifecycle of AI platform development, from design and implementation through deployment and sustainment

QUALIFICATIONS Eight (8) years of experience as a SWE in programs and contracts of similar scope, type, and complexity are 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:

• Proven experience building, deploying, and maintaining production systems at scale
• Experience designing and optimizing high-volume web application architectures for performance, scalability, and reliability
• Strong background in systems integration across diverse technologies, platforms, and services
• Hands-on experience with cloud engineering and solution deployment within AWS environments
• Proficiency in administering and deploying applications within Kubernetes-based environments
• Strong Python development skills for automation, infrastructure, and application development efforts
• Experience implementing observability and monitoring solutions using technologies such as APM, OpenTelemetry, Grafana, and Prometheus
• Familiarity with CI/CD pipelines, automation frameworks, and DevOps best practicesStrong understanding of infrastructure automation, deployment strategies, and operational excellence principles
• Strong change management, stakeholder engagement, and organizational influence skills
• Ability to operate effectively within ambiguous environments and establish structure for evolving requirements
• Strong analytical, troubleshooting, and problem-solving skills
• Excellent written and verbal communication skills
• Ability to collaborate effectively across multidisciplinary engineering and operational teams
• Experience supporting the full lifecycle of cloud-native applications and platform services from design through production operations

Desired Skills NICE-TO-HAVES

• Experience with AI inference serving technologies such as vLLM, LiteLLM, or similar platforms
• Experience developing solutions using agentic AI frameworks such as LangChain or comparable technologies
• Knowledge of vector databases, embedding models, and semantic search architectures
• Experience designing and supporting retrieval-augmented generation (RAG) solutions and AI-enabled applications
• Familiarity with large language model deployment, optimization, and inference workflows
• Experience with high-performance computing environments and distributed systems architectures
• Knowledge of scalable data processing, distributed computing, and resource optimization techniques
• Experience supporting enterprise AI platforms and machine learning infrastructure
• Familiarity with emerging AI technologies, frameworks, and platform capabilities
• Experience integrating AI services and infrastructure into cloud-native environments and production systems