> Markdown version of [/jobs/ext/1377234-senior-software-engineer-ai-platform](https://www.wearedevelopers.com/jobs/ext/1377234-senior-software-engineer-ai-platform). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Software Engineer, AI Platform - **Company:** Inc. (kai) - **Location:** San Jose, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Test Suite, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Unit Testing, Microsoft Azure, Cloud Computing, Code Review, Databases, Continuous Integration, Django Web Framework, Github, Python (Programming Language), Machine Learning, Parsing, Mockito, Prometheus, Azure DevOps Pipelines, Azure Machine Learning, Software Engineering, Software Vulnerability Management, Web Application Frameworks, AI Infrastructure, Data Logging, Flask (Web Framework), Delivery Pipeline, Large Language Models, Grafana, Prompt Engineering, Caching, Backend, Rate Limiting, Fastapi, Pytest, AI Platforms, Gitlab-ci, Integration Tests, Kubernetes, Production Code, Machine Learning Operations, Restful APIs, Code Restructuring, Dynatrace, Docker, Web Api, Microservices - **Published:** July 22, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=5e421f6f13ff99ee ## About the Role * 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. Experience 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) ## Description We are building an AI-powered cybersecurity platform that helps enterprises manage vulnerabilities at scale. Our AI team has built working services that analyze container images, standardize package data, extract natural language filters, and assess package maintenance - all powered by LLMs and intelligent automation., * Build the test suite from the ground up. You'll design the test infrastructure - unit tests with mocked LLM responses, integration tests against staging environments, and fixtures that make testing fast and reliable. You'll wire this into CI so nothing ships without passing tests. * Harden production services. Audit and fix security issues. Implement structured logging. Add health checks, metrics, and traces. * Improve the CI/CD pipeline. You'll add quality gates so the team catches issues before they reach production. * Refactor for maintainability. Extract shared patterns into reusable modules. Break apart oversized classes and reduce code duplication across services. * Fix dependency management. Introduce lock files for reproducible builds, remove unused dependencies, and resolve version inconsistencies across services. * Own the reliability and performance of our AI service fleet (Python/FastAPI microservices) * Build out observability - distributed tracing, latency dashboards, alerting on error rates and SLA breaches * Design and implement caching strategies, rate limiting, and circuit breakers for external API calls (Anthropic, Azure ML, package registries) * Collaborate with AI scientists on prompt engineering and output parsing, bringing engineering rigor to LLM integration patterns * Mentor mid-level engineers as the team grows, * 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. ## Related Videos - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [pytest: Simple, rapid and fun testing with Python](https://www.wearedevelopers.com/videos/213-pytest-simple-rapid-and-fun-testing-with-python) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) - [Automagic Configuration in Python](https://www.wearedevelopers.com/videos/363-automagic-configuration-in-python) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)