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About the role
As a Senior GenAI Developer, you will be responsible for designing, developing, and integrating advanced Generative AI capabilities with security platforms and applying data science techniques to improve decision-making and automation. You will bridge traditional backend engineering with modern AI/ML and data engineering practices, leveraging both the .NET and/or Python ecosystems to build intelligent, scalable systems such as Retrieval-Augmented Generation (RAG) pipelines, predictive models, and autonomous agents. Core Responsibilities: Assess workflows and map out opportunities to leverage AI, data science, and predictive modeling across security processes. Develop intelligent agents and automated content generation capabilities for security workflows. Enable automation across security processes, including threat analysis, access certification, incident response, and compliance reporting. Partner with cross-functional teams to translate business requirements into robust, AI-driven software solutions. Develop and integrate intelligent features into applications, combining LLMs, statistical models, and data insights. Build and optimize Retrieval-Augmented Generation (RAG) pipelines to improve AI accuracy. Develop scalable, secure backend services using .NET and/or Python ecosystems. Utilize orchestration frameworks (Microsoft Semantic Kernel or Microsoft Agentic etc.) to manage complex AI workflows. Ensure all AI integrations adhere to enterprise security, compliance, and governance standards. Required Skills & Qualifications: Bachelors degree in Computer Science, Information Technology, or a related field. 5+ years of experience in .NET development with expert-level proficiency in C#, .NET 8+, and Entity Framework Core. Hands-on experience with Large Language Models (LLMs), prompt engineering, and model fine-tuning. Proven experience with Semantic Kernel, OpenAI SDK, and vector stores (e.g., Azure AI Search). Strong knowledge of RESTful API design and database management. Experience with CI/CD pipelines via GitHub Actions or Azure DevOps, including containerization with Docker and Kubernetes. Preferred: Experience with Dotnet/Python for AI prototyping or scripting. Familiarity with building AI Agents and multi-agent systems. Relevant certifications such as Microsoft Certified: Azure AI Engineer Associate.
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