Software Engineer - AI/LLM Workflows

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Claude Grok

Perplexity Google

About CloudBees

CloudBees enables enterprises to deliver scalable, compliant, and secure software, empowering developers to do their best work. Seamlessly integrating into any hybrid and heterogeneous environment, CloudBees is more than a tool—it's a strategic partner in your cloud transformation journey, ensuring security, compliance, and operational efficiency while enhancing the developer experience across your entire software development lifecycle.

About the Role

CloudBees is looking for a creative and technically sharp Software Engineer to help architect and prototype AI/LLM-driven workflows that augment developer productivity. This is a hands-on engineering role focused on building agentic flows using tools like Goose, Unify MCP, AWS Q, and Gemini. This is a high-impact role that sits at the intersection of artificial intelligence and software delivery – ideal for someone passionate about pushing the boundaries of developer productivity and intelligent automation.

Key Responsibilities

Design, prototype, and test agentic workflows using platforms like Goose, Unify MCP, AWS Q, and Google Gemini
Build and iterate on AI-powered agents that can autonomously interact with systems, APIs, and developer tools
Translate product concepts into working demos and scalable pipeline implementations
Collaborate cross-functionally with product managers, ML engineers, and DevOps teams to refine user workflows
Optimize agent behavior for reasoning, autonomy, reliability, and safety
Contribute to ML pipeline development for deploying and monitoring agentic flows in production environments
Document architectures and share learnings across the engineering org

Required Qualifications

3+ years of experience with agentic systems and LLM
5+ years of hands-on experience in AI/ML engineering and LLM Ops
Proven knowledge & experience developing agentic flows with frameworks like Langchain, CrewAI, Agno, or custom solutions
Familiarity with prototyping and testing in environments with MCP enabled tools like Claude Desktop/Goose, AWS Q, and Gemini
Experience designing and maintaining pipelines for ML models, workflows, or LLM agents
Strong programming skills in Python and TypeScript
Comfortable working in cloud-native environments (AWS/GCP) and with DevOps pipelines (e.g., GitHub Actions, Argo)
Startup experience or demonstrated ability to build fast, iterate quickly, and wear multiple hats