Lead 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 Lead AI Engineer to lead our Agentic AI team as we scale our agentic offerings across all levels of the US government. Over the past year, we have seen the rapid adoption of our agent, Ace. We expect Ace to be handling much more complex tasks and workflows end-to-end and/or in cooperation with a human user as time goes on. The team is striving to make Ace an even more effective agent, focusing on planning, reliable execution over longer time horizon tasks, scaled tool use, Ace Skills, memory, and inter-agent coordination. In order to do this job well, we are looking for someone who can demonstrate a project built on LLMs that showcases your skill at getting them to do complex tasks. Experience leading agentic syst [... source excerpt omitted ...] entic systems using LLMs Hands-on work with multi-agent coordination, routing, and tool orchestration Passion for building autonomous and semi-autonomous systems that solve real customer problems This role is a full-time position located out of our office in Pittsburgh, PA. This role may require up to 10% travel Scope of Responsibilities Designs and builds multi-agent systems with tool use, memory, routing, and planning Develops Agent Skills and tools as modular, composable services that interact with backend systems, models, and data processing Assist with automated evaluation of agents, skills, and prompts across the entire product lifecycle Work with our Product and I [... source excerpt omitted ...] ster’s, or Doctorate in Computer Science, Computer Engineering, Data Science, or a related field Minimum 3 years of experience building and deploying ML or LLM-powered systems in production environments Minimum 2 years of experience leading or mentoring engineering teams in production environments Practical experience in building, developing, and productionizing machine learning systems Advanced software skills in Python Experience with common LLM algorithms and implementations Hands-on experience with AWS and cloud infrastructure A strong desire to learn and investigate new technologies Familiarity with Git source control management Ability to work collaboratively with li