AI Agent Engineer, AI Transformation

Psomagen, Inc.•Rockville, MD

2h•$70,000 - $140,000

About The Position

Psomagen is seeking a hands-on developer to lead the company-wide AI transformation. The role involves designing, building, and operating AI agents that work with the tools employees use, including Google Workspace, HubSpot, the in-house LIMS, and SAP. Working directly with the CEO and teams across the company, the engineer will identify opportunities to reduce manual work, deliver agents quickly, and maintain reliable production systems. This is a builder role rather than an advisory role. The engineer will develop agents using Hermes and comparable frameworks while keeping underlying models swappable. They will integrate agents with business systems using appropriate methods and defined permissions; build knowledge retrieval, evaluation, testing, logging, monitoring, and cost-control infrastructure; and establish role-based access, audit trails, and human approval steps for sensitive actions. The role also includes collaboration with LIMS development and bioinformatics teams, employee training and feedback, and evaluation of new AI models, tools, and practices. The position requires a bachelor's degree or equivalent experience in a relevant field, at least four years of professional software development experience with strong Python skills, and demonstrated experience building and operating LLM-based agents. The candidate must be legally authorized to work in the United States. The stated base salary range is $70,000 to $140,000, depending on experience and qualifications, plus a performance-based bonus. Benefits are listed as medical, dental, vision, 401(k), and PTO, with “etc.” indicating the list may not be exhaustive.

Requirements

Bachelor's degree or equivalent experience in computer science, software engineering, or a related field.
At least 4 years of professional software development experience, with strong Python skills.
Demonstrated experience building and operating LLM-based agents in production or substantial real-world use, including tool calling, multi-step workflows, retrieval, prompt and context design, and evaluation.
Current knowledge of frontier AI models and agent tooling, including the ability to explain trade-offs among models, frameworks, and cost.
Experience connecting software or agents to business systems, including authentication and permission design.
Strong communication skills and comfort working with non-technical colleagues to understand their workflows.
Awareness of data security and privacy requirements when AI handles customer, financial, or health-related data.
Legally authorized to work in the U.S.

Nice To Haves

Experience with Hermes or similar open agent frameworks and Google Cloud.
Hands-on experience with Google Workspace administration and APIs, Apps Script, and HubSpot.
Experience with LIMS, SAP, or other laboratory and enterprise operations systems.
Domain knowledge of sequencing services or genomics, especially bioinformatics, including pipelines, workflow tools such as Airflow, QC, and reporting.
Experience in a regulated environment, such as CLIA/CAP, ISO 9001, or ISO 27001.
Experience with SQL and data pipelines.

Responsibilities

Design, develop, and operate AI agents using Hermes and comparable agent frameworks, keeping the underlying models swappable.
Connect agents to Google Workspace, HubSpot, the in-house LIMS, SAP, and other business systems, selecting suitable integrations and defining permissions for reading, drafting, and taking actions.
Work with sales, project management, lab operations, finance, regulatory/quality, and IT to identify valuable workflows and turn them into working agents.
Build infrastructure for knowledge retrieval over company documents, agent-output evaluation and testing, logging, monitoring, and cost control.
Implement role-based access, audit trails, and human approval steps for actions involving customers, finance, or regulated records.
Collaborate with LIMS development and bioinformatics teams to fit agents into existing systems and pipelines.
Train employees to use agents, collect feedback, and improve deployed solutions.
Track developments in AI models, tools, and practices, and introduce those that provide practical business value.