AI Engineer, Agentic Interactions

Govini•Pittsburgh, PA

2h•Onsite

About The Position

We are seeking an experienced AI Engineer to join 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 interact with all Ark interfaces in an agentic manner 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 interact with frontend interfaces agentically. Experience designing front-end architectures with explicit state and action models suitable for AI-driven interaction (agent-controlled UI, tool invocation, and deterministic workflows). Experience integrating and working with LLMs, with a strong understanding of their capabilities and limitations Experience integrating and working with agent frameworks like Claude Agent SDK or 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: Experience designing and deploying complex agentic systems using LLMs Hands-on work with multi-agent coordination, routing, and tool orchestration Love coding agents This role is a full-time position located out of our office in Pittsburgh, PA. This role may require up to 10% travel

Requirements

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
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 little supervision
A burning desire to work in a challenging fast-paced tech environment
Experience designing front-end architectures with explicit state and action models suitable for AI-driven interaction (agent-controlled UI, tool invocation, and deterministic workflows).
Experience integrating and working with LLMs, with a strong understanding of their capabilities and limitations
Experience integrating and working with agent frameworks like Claude Agent SDK or 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

Nice To Haves

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 Claude Code and GenAI coding best practices.
Experience designing and deploying complex agentic systems using LLMs
Hands-on work with multi-agent coordination, routing, and tool orchestration
Love coding agents

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 Implementation organizations to find solutions to our most vexing challenges applying agents to our customer problems

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