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We’re building an internal, AI‑powered developer platform designed to help engineering teams deliver software faster, more safely, and with greater confidence. The platform offers reusable workflow templates, AI‑assisted automation, CLI tooling, documentation, and evaluation pipelines-everything teams need to adopt AI‑enhanced engineering practices in a secure and scalable way. You will not be a dedicated security engineer, but you will be expected to think carefully about how workflows can be abused, how agents can misbehave, how secrets can leak, and how permissions should be constrained. You should care about what happens when automation fails, when an AI agent produces unsafe output, or when a workflow is used in an unexpected way. We are looking for an AI-native platform engineer who can build the tools and workflows that help engineering teams adopt AI safely and effectively. You should enjoy building reusable systems, improving developer experience, automating repetitive work, and turning fast-moving AI capabilities into practical engineering infrastructure. We’re looking for someone already fluent with modern AI‑native development tools. You should be comfortable using tools like GitHub Copilot, Cursor, Windsurf, Kiro, or similar to prototype, build, review, and iterate quickly.
Job Responsibility:
Build AI-Powered Developer Workflows
Develop Internal Platform Capabilities
Own AI Evaluation and Quality Gates
Support Secure and Scalable Platform Operations
Drive Adoption Across Engineering Teams
Requirements:
BSc/MSc in Computer Science or related field
Minimum 3-5+ years as a Platform Engineer
Infrastructure-as-code tools such as Terraform, Bicep, Pulumi, or CDK
Proficient with AI‑assisted development tools - You regularly use AI coding tools or agentic workflows to accelerate software delivery
Strong prompt‑crafting and AI evaluation skills- You can design effective prompts, critically assess AI‑generated code, and understand when AI tools are dependable versus when human oversight is required
CI/CD and automation expertise - You have hands‑on experience building and maintaining automated pipelines
Deep knowledge of GitHub Actions or similar systems - You are comfortable with reusable workflows, composite actions, pipeline‑as‑code patterns, and automated validation
Python engineering capability- You can build evaluation scripts, custom validators, SDK integrations, automation utilities, and platform tooling
Practical prompt engineering experience - You’ve designed, tested, and iterated on prompts within real engineering workflows
Identity and security fundamentals — You understand OIDC, workload identity federation, secrets management, least‑privilege access, and secure automation patterns
Configuration‑as‑code proficiency — You work comfortably with YAML, Markdown, declarative configuration, and docs‑as‑code practices
Nice to have:
AI evaluation frameworks such as Azure AI Evaluation SDK, promptfoo, RAGAS, DeepEval, or similar
Agentic AI frameworks such as LangChain, CrewAI, AutoGen, OpenAI Assistants API, or similar
Internal developer platforms, developer tooling, or Developer Experience engineering
Software supply chain security concepts such as dependency scanning, action pinning, and SBOMs
Statistical methods for evaluating non-deterministic systems
AI safety and adversarial testing, including prompt injection and OWASP Top 10 for LLM applications
Static site generators and docs-as-code pipelines
Open-source contributions to developer tooling or automation projects
What we offer:
Smooth integration and a supportive mentor
Pick your working style: choose from Remote, Hybrid or Office work opportunities
Different working hours to suit your needs
Sponsored certifications, trainings and top e-learning platforms
Private Health Insurance custom-made for you
Individual coaching sessions or accredited Coaching School