Lead AI Engineer: Multi-Agent Systems & LLMs

Air; Pittsburgh (Allegheny County); On-site; USD 150,000 - 230,000; Full time

Job summary

Air is seeking an experienced Lead AI Engineer to head the Agentic AI team in scaling agentic offerings for US government customers. You will oversee architecture, planning, and day-to-day leadership of Data Scientists and ML Engineers, focusing on planning, long-horizon execution, tool use, and inter-agent coordination.

The role requires leading design and productionizing complex agentic systems built on LLMs, with strong emphasis on observability, evaluation, and reliability in fast-paced,

Qualifications

• Bachelor's/Master's/Doctorate in CS/CE/DS or related field.
• Minimum 3 years of experience building and deploying ML/LLM-powered systems in production.
• Minimum 2 years of experience leading or mentoring engineering teams in production environments.
• Advanced software skills in Python.
• Experience with common LLM algorithms and implementations.
• Hands-on experience with AWS and cloud infrastructure.
• Familiarity with Git source control management.
• Ability to work collaboratively with little supervision.

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 product lifecycle.
• Work with Product and Implementation teams to apply agents to customer problems.

Skills

Python

LLMs

Multi-agent systems

Leadership

Cloud infra

Telemetry

Agent frameworks

Agile leadership

Git

US Citizenship

Education

Bachelor's/Master's/PhD in CS/CE/DS

Tools

AWS

Claude SDK

OpenAI SDK

Git

Job description

Air is seeking an experienced Lead AI Engineer to head the Agentic AI team in scaling agentic offerings for US government customers. You will oversee architecture, planning, and day-to-day leadership of Data Scientists and ML Engineers, focusing on planning, long-horizon execution, tool use, and inter-agent coordination.

The role requires leading design and productionizing complex agentic systems built on LLMs, with strong emphasis on observability, evaluation, and reliability in fast-paced,