Senior Python Backend Engineer: AI, Kubernetes & APIs

Erias Ventures, LLC.; Columbia (MD); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

401(k) With Immediate Vesting

Spot Bonuses

Professional Development Bonuses

Medical Coverage

Vision Coverage

Dental Coverage

Long-Term Disability Insurance

Short-Term Disability Insurance

Group Life Insurance

Wellness Reimbursement

Paid Time Off

Flexible Work Schedules

Birthday Off

Amazon Prime Membership

Monthly Internet Reimbursement

Technology and Productivity Allowance

Company Events

Company-Paid Professional Development

Job summary

Erias Ventures, LLC. seeks a skilled software engineer to design and implement Python backend components for an agentic AI platform in a US government-ready environment.

The role requires hands-on Kubernetes deployment, building MCP servers, and creating reusable AI skills while integrating Java APIs and implementing safety measures such as privacy protections and prompt-injection defenses.

Qualifications

• Requires 5 years of software engineering experience with a strong focus on Python backend development.
• Bachelor's degree or equivalent experience; strong production AI integration exposure.
• U.S. citizenship with ability to obtain and maintain a U.S. government security clearance.

Responsibilities

• Design and develop Python components for an agentic AI platform.
• Deploy and scale applications in Kubernetes.
• Build MCP servers and reusable skills for safe agent use of internal and external services.
• Collaborate to connect Java-based APIs and implement enterprise AI safeguards.

Skills

Python backend development

Applied AI concepts

Large Language Models

REST APIs

Microservices

Prompt engineering

Education

Bachelor's degree or equivalent

Tools

Kubernetes

Docker

CI/CD

Linux

REST APIs

Job description

Erias Ventures, LLC. seeks a skilled software engineer to design and implement Python backend components for an agentic AI platform in a US government-ready environment.

The role requires hands-on Kubernetes deployment, building MCP servers, and creating reusable AI skills while integrating Java APIs and implementing safety measures such as privacy protections and prompt-injection defenses.