AI Engineer

Company Description

Air is the leader in Enterprise Readiness. Our mission is to establish readiness as a real-time condition that is continuously achieved. Today, a dangerous Readiness Gap exists between what the front line needs and what is delivered. Our AI-native platform, Air Enterprise Readiness, aligns development, production, delivery, and sustainment into one coordinated execution system for government agencies and industrial suppliers. By revealing true capacity, exposing real constraints, coordinating resources, and executing at the speed of operational demands, the front line gets what it needs to succeed.

Job Description

We are seeking an experienced Senior AI Engineer to join our Agentic AI team as we scale our AI capabilities across all levels of the U.S. government.

Over the past year, we have seen rapid adoption of our AI agent, Ace. We are building the systems and infrastructure required to move agentic AI beyond demos and prototypes into reliable, production-grade software operating against complex, real-world problems.

This role sits at the intersection of applied AI and systems engineering. You will design and build agent architectures, model integrations, tools, evaluation systems, and infrastructure that enable increasingly capable AI systems. You will work across the AI stack - from experimentation and model behavior to production services and distributed infrastructure.

In order to do this job well: we are looking for engineers who understand that building great AI products requires more than calling a model API. You should be comfortable reasoning about how agents use context, tools, models, memory, and compute, and turning those ideas into reliable production systems.

This role is a full-time position based in our Pittsburgh, PA office or open to Remote Opportunities.

This role may require up to 25% travel, including periodic travel to our Pittsburgh, PA and Arlington, VA offices for team collaboration, planning activities, and in-person meetings.

Scope of Responsibilities

• Design, build, and improve production agentic AI systems used to solve complex real-world problems.
• Develop agent architectures for reasoning, planning, tool use, context management, memory, and multi-step task execution.
• Build tools and capabilities that allow agents to securely interact with data, APIs, code, and external systems.
• Develop model and inference infrastructure supporting multiple commercial and open-weight language models.
• Evaluate new models, inference techniques, and emerging AI capabilities and determine how they can improve our production systems.
• Build automated evaluation frameworks to measure agent quality, reliability, task completion, and regressions.
• Develop datasets, benchmarks, and evaluation methodologies for complex agentic workflows.