Software Engineer (AI Infrastructure)

TL;DR

Software Engineer (AI Infrastructure): Building and optimizing scalable infrastructure for AI model inference, RAG, and autonomous agents with an accent on production-grade reliability and high-volume web architecture. Focus on designing inference services, implementing observability solutions, and automating infrastructure provisioning via IaC.

Location: Onsite in Columbia, Maryland. TS/SCI with Poly clearance required.

Company

hirify.global provides AI-powered decision intelligence solutions for national security, supply chain management, and digital identity.

What you will do

• Design, implement, and optimize infrastructure for AI model inference at scale.
• Support production AI services including retrieval augmented generation (RAG) and autonomous agents.
• Implement monitoring, logging, and observability solutions using APM, OpenTelemetry, Grafana, and Prometheus.
• Automate infrastructure provisioning and configuration using Infrastructure-as-Code (IaC) principles.
• Ensure high availability, reliability, and security of AI platform components.
• Provide technical guidance and informal mentorship to junior engineers.

Requirements

• Clearance: TS/SCI w/Poly
• 8+ years of relevant experience (or Bachelor's degree in a technical discipline + 4+ years of experience).
• Strong Python programming skills and hands-on experience with cloud engineering in AWS.
• Proficiency with Kubernetes administration and deployment patterns.
• Experience with high-volume web application architecture and systems integration.
• Familiarity with CI/CD pipelines and DevOps practices.

Nice to have

• Experience with AI inference serving technologies such as vLLM or LiteLLM.
• Previous experience with agentic frameworks like LangChain.
• Knowledge of vector databases and embedding systems.
• Experience with high-performance computing or distributed systems.

Culture & Benefits

• Work in a high-impact environment providing critical intelligence for national security.
• Opportunity to drive the adoption of emerging AI technologies and practices.
• Collaborative engineering culture focused on solving complex, underspecified systems challenges.
• Equal opportunity employer environment.