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Delinea is an identity security platform that uses AI-powered intelligence to govern human and machine identities across modern enterprises. The Iris AI team builds the AI-driven capabilities of the platform, leveraging large language models to detect anomalies in audit data, enhance authorization decisions, and deploy intelligent agents.
As a Senior Software Engineer on the Iris AI team, you will design, build, and operate cloud services that power Delinea's AI features. You'll work across the full stack: backend services in C#/.NET, Python, and Go; LLM integration and inference; retrieval-augmented generation (RAG); tool calling; and agentic workflows. You'll take end-to-end ownership of delivery, including infrastructure-as-code with Terraform, CI/CD pipelines, monitoring, and production incident response.
Key responsibilities include building and operating scalable, highly available backend services on Azure and Kubernetes; developing LLM-powered features using Azure AI Foundry and similar platforms; designing event-driven, asynchronous microservice architectures; and collaborating with product, UX, and platform teams on code and design reviews. You'll leverage AI-assisted development tools (Claude Code, GitHub Copilot, Cursor) in your daily workflow, using them to design, write, and review code to the same standard as hand-written code.
You'll report to the Director of Engineering, Iris AI. This is a hybrid role based in Mexico City.
Required qualifications: Bachelor's in Computer Science or equivalent; 8+ years designing and building production backend services in C#/.NET, Python, or Go; hands-on experience building products with LLMs (inference, structured outputs, RAG, tool calling, agents); proficiency with AI-assisted development; strong understanding of distributed systems, API design, and microservices; experience with a major cloud provider; and knowledge of multi-tenant SaaS architecture at scale.
Bonus experience includes building tools and APIs for AI agents (MCP servers, function-calling interfaces); ML and MLOps depth (fine-tuning, evaluation, model deployment); Kubernetes, Terraform, and CI/CD expertise; and background in identity security, PAM, or Zero Trust architectures.
