AI Engineer, Internal Enablement & Productivity

artificial intelligence and robotics laboratory (itu air lab); Pittsburgh (Allegheny County); On-site; USD 130,000 - 210,000; Full time

Job summary

Air seeks an experienced AI Engineer to scale internal AI enablement across departments by deploying agents and workflows. This full-time role is based in Arlington, VA or Pittsburgh, PA and may require up to 25% travel.

You will leverage best-in-class AI tools to automate complex tasks and drive measurable improvements in productivity and cost efficiency. You’ll design and deploy agentic workflows, integrate LLMs, and ensure observability and reliability through telemetry, evaluation, and

Qualifications

• U.S. Citizenship is required.
• Bachelor’s, Master’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.
• Advanced software skills in Python and other programming languages.
• Experience with common LLM algorithms and implementations, including coding agents (e.g. Claude Code and GenAI coding best practices).
• Hands-on experience with cloud infrastructure (e.g. AWS, GCP).
• A strong desire to learn and investigate new technologies.
• Familiarity with Git source control management.
• Ability to work collaboratively with little supervision.
• A burning desire to work in a challenging fast-paced tech environment.

Responsibilities

• Designs and builds multi-agent systems with tool use, memory, routing, and planning to automate internal business processes and enhance employee capabilities.
• Develops Agent Skills and tools as modular, composable services that interact with backend systems, models, and data processing to increase internal team velocity.
• Contribute to the technical architecture and engineering standards for internal agentic systems, ensuring scalability, reliability, and maintainability across the organization.
• Assist with automated evaluation of agents, skills, and prompts across the entire product lifecycle to ensure reliable internal tools.
• Partner with internal Product, Engineering, GTM, and Operations teams to identify and implement AI-driven solutions to optimize their workflows and reduce operational costs.

Skills

LLM frameworks

Agent frameworks

Observability

Ambiguity handling

Startup experience

Education

Bachelor’s/Master’s/Doctorate in CS/CE/DS

Tools

Claude Agent SDK

Google Anti-Gravity

OpenAI Agent SDK

AWS

GCP

Job description

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 AI Engineer to join our AI Enablement team, focused on rapidly increasing internal employee productivity and operational efficiency across the company by scaling robust AI integrations and agentic workflows. This role will be central to designing and deploying agents to handle complex, end-to-end tasks, driving measurable improvements in how our core teams (e.g., Product, Engineering, Operations, and Customer Success) work. This strategic and execution-focused role will lead our internal AI Enablement roadmap, initially focusing deeply on improving core departments (e.g., Sales, Marketing, Product, Engineering, Operations, and Customer Success) through agentic automation.

You’ll leverage best-in-class AI tools and AI agents (e.g. Gemini and other LLMs) to dramatically increase employee productivity, drive down operational costs, and enable our teams to deliver faster, better outcomes at scale. Your goal is to build our foundation for an AI-enabled future, implementing AI-driven workflows and agents throughout the organization, starting with internal operations and coupling workflows across key functions like GTM.

To do this job well, we are looking for someone who can demonstrate project(s) built on LLMs that showcases your skill at reliably automating complex tasks.

• Experience integrating and working with LLMs, with a strong understanding of their capabilities and limitations
• Experience integrating and working with agent frameworks (e.g. Claude Agent SDK, Google Anti-Gravity, OpenAI Agent SDK)
• Experience building observability, evaluation, and feedback loops for agent behavior (telemetry, prompt evaluation, regression testing, and reliability metrics).
• Experience working in highly ambiguous environments, and operate with urgency
• Startup experience, particularly in scaling products from zero to one

Strong Candidates have:

• Deep experience leveraging agents across applications and business workflows to drive meaningful impact
• Experience designing and deploying complex agentic systems using LLMs (e.g. deep research)
• Hands-on work with multi-agent coordination, routing, and tool orchestration

This role is a full-time position located out of our office in Arlington, VA or Pittsburgh, PA. This role may require up to 25% travel.

Scope of Responsibilities

• Designs and builds multi-agent systems with tool use, memory, routing, and planning to automate internal business processes and enhance employee capabilities.
• Develops Agent Skills and tools as modular, composable services that interact with backend systems, models, and data processing to increase internal team velocity.
• Contribute to the technical architecture and engineering standards for internal agentic systems, ensuring scalability, reliability, and maintainability across the organization.
• Assist with automated evaluation of agents, skills, and prompts across the entire product lifecycle to ensure reliable internal tools.
• Partner with internal Product, Engineering, GTM, and Operations teams to identify and implement AI-driven solutions to optimize their workflows and reduce operational costs.

How We Define Success (Key Metrics)Internal Team Enablement (Operations, Customer Success, Engineering)

• Operational Cost Savings: Reduce operational expenses for targeted teams through workflow automation via AI agents.
• Employee Productivity: Achieve measurable increases in internal team capacity and velocity through AI-driven workflows and tools.
• Internal Adoption Rate: Achieve a high adoption rate of new AI agents and enablement tools within key departments within the first 90 days.

• Accelerated Development: Reduce feature development timelines through AI-assisted coding, documentation, and testing tools.

Qualifications

• U.S. Citizenship is required

Required Skills:

• Bachelor's, Master’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
• Practical experience in building, developing, and productionizing machine learning systems
• Advanced software skills in Python and other programming languages
• Experience with common LLM algorithms and implementations, including coding agents (e.g. Claude Code and GenAI coding best practices).
• Hands-on experience with cloud infrastructure (e.g. AWS, GCP)
• A strong desire to learn and investigate new technologies
• Familiarity with Git source control management
• Ability to work collaboratively with little supervision
• A burning desire to work in a challenging fast-paced tech environment
• A burning desire to work in a challenging fast-paced tech environment

Desired Skills:

• Current possession of a U.S. security clearance, or the ability to obtain one with our sponsorship
• Experience in or exposure to the nuances of a startup or other entrepreneurial environment
• Experience with modern front-end frameworks (e.g., React, Next.js, or similar) and API-driven UI architectures