Senior .Net Developer Ai
at Delinea
Mexico City, hybrid, senior, full time, 8+ years
posted 12 Aug 2026
- C#
- Azure
- SaaS
- Go
- Distributed Systems
- LLM
- Cursor
- Microservices
- GitHub Copilot
- Azure AI Foundry
- API Design
- OpenAI API
- Anthropic API
- MLOps
- Terraform
- Claude Code
- CI/CD
- .NET
- RAG
- Google Vertex AI
- Amazon Bedrock
- Python
- Kubernetes
About the role
Posted: 2026-08-12
What you would do
- Build and operate scalable, highly available backend services in C#/.NET, Python, and Go, utilizing Azure and Kubernetes.
- Develop LLM-powered features using platforms such as Azure AI Foundry: model integration, context pipelines, RAG, tool calling, agents, and evaluation.
- Design event-driven, asynchronous architectures across microservices.
- Take end-to-end responsibility for delivery, including infrastructure-as-code with Terraform, CI/CD pipelines, monitoring, and production incident response.
- Collaborate with product, UX, and other platform teams; actively participate in code and design reviews.
- Leverage AI in your daily workflow; use coding agents to design, write, and review code held to the same standard as hand-written code.
What they are looking for
- Bachelor’s in Computer Science or related field, or equivalent experience.
- 8+ years of experience designing and building production backend services in C#/.NET, Python, or Go, and engaging across the full software development lifecycle.
- Hands-on experience building products with LLMs, including inference, structured outputs, RAG, tool calling, or agents (e.g., Azure AI Foundry, OpenAI API, Amazon Bedrock, Google Vertex AI, Anthropic API, or similar).
- Proficiency with AI-assisted development (e.g., Claude Code, GitHub Copilot, Cursor), with emphasis on spec-driven development, careful context management, and verified, tested output.
- Strong understanding of distributed systems, API design, and microservices, as well as experience with a major cloud provider.
- Sound knowledge of multi-tenant SaaS architecture and the challenges of operating at scale.
- Experience building tools, APIs, or integrations that AI agents call (e.g., MCP servers, function/tool-calling interfaces, plugins).
- Depth in ML and MLOps: fine-tuning, evaluation harnesses, and model deployment in production.
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