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# Software Engineer (AI Infrastructure)

- **Company:** BigBear.ai, Inc.
- **Location:** Columbia, MD, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Engineering, Encodings, DevOps, Distributed Systems, Python (Programming Language), Performance Tuning, Prometheus, Systems Integration, Web Applications, AI Infrastructure, Data Logging, High Performance Computing, System Availability, Grafana, AI Platforms, Kubernetes, Infrastructure Automation Frameworks, BIG-IP Access Policy Manager (APM)
- **Published:** July 13, 2026
- **Apply:** https://www.clearancejobs.com/jobs/9027837/software-engineer-ai-infrastructure

## About the Role

* 8+ years of relevant experience, or Bachelor's degree in a technical discipline + 4+ years of experience
 * Clearance: TS/SCI w/Poly
 * Proven experience building and maintaining production systems at scale
 * Experience with high-volume web application architecture and performance optimization
 * Strong background in systems integration across diverse technologies and platforms
 * Hands-on experience with cloud engineering in AWS
 * Proficiency with Kubernetes administration and deployment patterns
 * Strong Python programming skills
 * Experience implementing observability solutions (APM, OpenTelemetry, Grafana, Prometheus)
 * Familiarity with CI/CD pipelines and DevOps practices
 * Strong change management and organizational influence skills
 * Ability to thrive in ambiguous environments and create structure where needed
 * Excellent communication and collaboration skills

What we'd like you to have

 * Experience with AI inference serving technologies (vLLM, LiteLLM, etc.)
 * Previous experience with agentic frameworks (LangChain)
 * Knowledge of vector databases and embedding systems
 * Experience with high-performance computing or distributed systems

## Description

* Design, implement, and optimize infrastructure for AI model inference at scale
 * Support the development and maintenance of production AI services and applications, including retrieval augmented generation (RAG), autonomous agents, and emerging technologies
 * Navigate ambiguity and define solutions for underspecified systems and requirements
 * Drive adoption of new technologies and practices across engineering teams
 * Implement monitoring, logging, and observability solutions for AI services
 * Automate infrastructure provisioning and configuration using Infrastructure-as-Code (IaC) principles
 * Ensure high availability, reliability, and performance of AI platform components
 * Contribute to security best practices for AI systems and data
 * Provide technical guidance and informal mentorship to junior engineers


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