AI-Native SDLC Engineer

About OpenVPN:

OpenVPN is a globally recognized leader in secure networking solutions, providing cutting-edge VPN and cybersecurity services that enable businesses and individuals to connect safely and privately. With a strong commitment to innovation, transparency, and trust, OpenVPN empowers organizations worldwide to build resilient, secure digital infrastructures.

We are now extending this philosophy internally, using AI to transform how our teams design, operate, and scale our Cipherscale product line.

CipherScale is an AI-native Zero Trust Network Access (ZTNA) platform built for modern hybrid teams. We enable seamless, secure access at scale—unifying Private apps, SaaS, and Internet access under a single, continuously enforced security layer.

The Role

This is a hands-on platform + systems engineering role where you will design, implement, and operationalize CipherScale’s fully autonomous, multi-agent software development lifecycle.

This role is about execution and is responsible for turning CipherScale’s AI-Native SDLC Governance Strategy into a working, production-grade engineering platform where AI Agents do the work, Artifacts and Git become the communication interface, and Humans approve and supervise rather than execute.

If you are a systems engineer and platform builder with significant experience in operating at the intersection of Distributed systems, DevOps, AI/LLM engineering, Software architecture, and IT Organizational design. If you think in event-driven workflows and design for determinism and traceability. If you anticipate failure modes before they occur and understand that governance is as important as automation, this role is a great match for you.

Our philosophy is that we are a small, closely-knit team, and we care deeply about you:

• Competitive pay rates

• Fully remote work environments

• Permanent employment with benefits

• A greenfield opportunity to build a fully AI-driven engineering organization

• Ownership of CipherScale’s AI-native execution layer

• The chance to define and be at the forefront of how leading businesses transform into AI-Native SDCL organisations

• Real authority to design and implement—not just advise

Important: This will be a permanent employment opportunity for candidates based in Spain, Germany, and Poland.

Requirements

Must-Have Requirements:

• 7+ years in software engineering, with experience in Platform engineering, DevOps / CI/CD systems, Event-driven architectures

• Proven experience building GitHub advanced repository automations, Webhook-driven systems, and Automated PR workflows

• Strong experience with LLM-based systems (tool calling, structured outputs, schema validation), Multi-agent orchestration or workflow engines, OpenAI / Anthropic APIs in production systems

• Deep understanding of Idempotency, Race conditions, Escalation patterns, and Deterministic system design

• Experience implementing policy-as-code

• Strong familiarity with artifact-driven SDLC models

• Solid architectural judgment and production experience, and experience leading engineering transformations

Nice-to-Have:

• Experience with n8n, Temporal, Prefect, or similar orchestration frameworks

• Background in Security platforms, Infrastructure systems, or zero-trust architectures

Core Role Responsibilities

Design and Implement CipherScale AI-Driven SDCL

• Architect and implement CipherScale’s AI-driven software development lifecycle.

• Ensure the SDLC process remains structured, traceable, and reliable as automation increases.

• Establish scalable patterns for AI-native development across the organization.

Workflow Automation

• Design and implement automated engineering & QA workflows that connect product strategy, engineering execution, testing, and deployment.

• Build systems that allow engineering processes to be triggered by structured inputs and outputs while ensuring workflows remain deterministic, reliable, and observable.

• Implement mechanisms that prevent failure loops, race conditions, and cascading errors in automated systems.

AI Agents Infrastructure

• Design and build agents that assist with engineering execution, validation, and quality control.

• Integrate AI capabilities into development workflows while maintaining strict governance and traceability.

• Develop reusable patterns for safely incorporating AI into engineering pipelines.

• Continuously evaluate and improve the performance and reliability of workflows and agents

Engineering Governance & Quality

• Establish standards and safeguards that ensure workflows remain secure, auditable, and maintainable.

• Implement automated validation mechanisms for engineering artifacts, code quality, and system consistency.

• Ensure that teams can maintain control and oversight over automated processes and AI Agents.

Observability, Reliability & Continuous Improvement

• Measure and optimize engineering cycle times, defect rates, and automation effectiveness.

• Continuously refine systems based on feedback from engineering teams and production outcomes.

• Ensure the platform evolves safely as automation and AI capabilities expand.

Cross-Functional Collaboration

• Work closely with engineering, product, QA, and IT operations teams to evolve development workflows.

• Ensure teams can effectively and smoothly transition into an AI Native SDLC Workflow while embracing and collaborating with AI-native engineering systems.