Hybrid AI Integration Engineer (Python, Kubernetes)

Phoenix Operations Group; Columbia, Northern (MD, KY); Hybrid; USD 120,000 - 170,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

Short-term and long-term disability

401(k) with company contribution

Paid time off

Paid holidays

Birthday leave

Referral bonuses

Open-source bonuses

Professional development

Job summary

Phoenix Operations Group in Columbia, Maryland is seeking a Software Engineer to build secure AI integrations and agent capabilities for enterprise data platforms. This hybrid role requires 1–2 days onsite per week.

You will develop Python backend services, deploy Kubernetes workloads, and connect AI agents with APIs and databases while implementing privacy safeguards and testing automation.

Qualifications

• Bachelor’s degree in a related field plus five years of experience; master’s plus three; or high school diploma with nine years of experience.
• Strong Python development and hands-on Kubernetes experience.
• Production experience integrating LLM APIs or open-source models.

Responsibilities

• Develop Python backend services, MCP servers, and reusable AI tools.
• Deploy and scale Kubernetes workloads.
• Connect AI agents with APIs, databases, and microservices.
• Implement privacy safeguards, prompt-injection defenses, and output validation.
• Automate testing, troubleshoot issues, and collaborate across engineering teams.

Skills

Python development

Kubernetes

AI integration

REST APIs

Git

Docker

CI/CD

Linux

Microservices

LLM APIs

Education

Bachelor’s degree + 5 years experience

Master’s degree + 3 years experience

High school diploma + 9 years experience

Tools

Kubernetes

Git

Docker

CI/CD

REST APIs

Linux

Job description

Phoenix Operations Group in Columbia, Maryland is seeking a Software Engineer to build secure AI integrations and agent capabilities for enterprise data platforms. This hybrid role requires 1–2 days onsite per week.

You will develop Python backend services, deploy Kubernetes workloads, and connect AI agents with APIs and databases while implementing privacy safeguards and testing automation.