Senior Software Engineer, AI Platform

• 4+ years of professional software engineering experience with a strong backend focus
• Deep Python expertise — not scripting, but well-structured production code. You understand when to use dataclasses vs Pydantic, how async/await actually works, and why global variables make testing painful
• Testing as a core discipline. You've built test suites for services with external dependencies. You're comfortable with pytest, mocking, fixtures, and know how to test code that calls third-party APIs without calling them
• FastAPI or equivalent modern Python web framework experience (Django REST Framework, Flask with production patterns). You've designed and maintained REST APIs that other teams depend on
• Azure or equivalent cloud platform experience. You've worked with managed container services, Kubernetes, managed databases, identity/auth systems, and CI/CD in a cloud environment. Azure preferred; AWS/GCP experience transfers well
• CI/CD pipeline engineering. You've added test gates, lint checks, and automated quality enforcement to build pipelines.

with Azure DevOps Pipelines, GitHub Actions, or GitLab CI

• Docker and containerization. You've written production Dockerfiles, understand multi-stage builds, and have debugged container networking and configuration issues
• Strong code review and collaboration skills. You'll be working with AI scientists who are strong in their domain but still developing engineering practices. You need to raise the bar without creating friction

• Experience working with LLM provider APIs (Anthropic, OpenAI, Azure OpenAI) — understanding token limits, prompt design, structured output parsing, and retry patterns
• Experience with structured logging (structlog), observability tools (OpenTelemetry, Prometheus, Grafana), or APM platforms
• Exposure to cybersecurity, vulnerability management, or compliance-sensitive environments
• Experience on a small engineering team at a startup, where you owned services end-to-end
• Familiarity with RAG patterns, embedding pipelines, or vector databases (not required, but a plus for growth)

• High-impact ownership. You'll build the engineering foundation for an AI platform that protects enterprises from security vulnerabilities.
• Growth into AI/ML engineering. As our AI capabilities mature into fine-tuning, custom model serving, and evaluation frameworks, you'll grow into MLOps and AI infrastructure. We'll invest in your development.
• Shape the team. You'll have input into hiring decisions as we grow the engineering side of the AI team. The engineers we hire next will be your peers and reports.
• Work with cutting-edge AI. You'll work daily with Claude, and other frontier models — not training them but engineering the systems that make them useful in production.