> Markdown version of [/jobs/ext/124871-kubernetes-cloud-engineer-eks](https://www.wearedevelopers.com/jobs/ext/124871-kubernetes-cloud-engineer-eks). 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). --- # Kubernetes Cloud Engineer (EKS) - **Company:** OpenKyber LLC - **Location:** United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** .NET Framework, Artificial Intelligence, Amazon Web Services, Audit Trail, Automation of Tests, Microsoft Azure, C Sharp (Programming Language), Cloud Engineering, Computer Programming, Continuous Integration, Extract Transform Load (ETL), Distributed Systems, Python (Programming Language), Performance Tuning, Role-Based Access Control, Azure Machine Learning, Software Safety, Search Technologies, Secure Coding, Software Engineering, AI Infrastructure, Azure Data Factory, Large Language Models, Multi-Agent Systems, Multi-Cloud, Generative AI, AI Platforms, Kubernetes, Deployment Automation, HuggingFace, AWS Data Analytics, Azure AKS, Machine Learning Operations, Virtual Agents, Api Gateway, Serverless Computing, Databricks - **Published:** May 13, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=3349de217fd5b313 ## About the Role Do you have experience in Python?, * 6+ years of software engineering experience with strong development fundamentals. * 2+ years of hands-on experience with GenAI/LLM technologies in production environments. * Strong programming experience in: Python, C#, .NET. * Experience building enterprise AI applications using: RAG architectures, vector databases, embeddings and semantic search, multi-agent orchestration. * Hands-on experience with Azure technologies including: Azure OpenAI, Azure AI Search, Azure ML, AKS, Azure Functions, Azure Data Factory, Azure Databricks. * Experience with AWS services such as: Bedrock, SageMaker, Lambda, API Gateway, EKS, EMR. * Strong understanding of: distributed systems, secure coding practices, CI/CD, performance optimization, AI governance and observability. Preferred Qualifications * Experience with Hugging Face, MLflow, Ollama, vLLM, or Triton. * Knowledge of vector search optimization (HNSW/IVF) and GPU scheduling. * Experience with Responsible AI governance and AI safety frameworks. * Familiarity with multi-cloud AI deployments and Kubernetes-based AI infrastructure. * Relevant cloud and AI certifications are a plus. ## Description OpenKyber is seeking an AI Engineer on behalf of our client in Washington, DC. This role is ideal for a hands-on engineer with strong software development experience and deep expertise in Generative AI, Retrieval-Augmented Generation (RAG), Agentic AI systems, and cloud-native AI platforms across Azure and AWS. The ideal candidate will have experience designing and deploying scalable AI applications, secure multi-agent systems, and enterprise-grade AI infrastructure in production environments., * Design and implement enterprise-scale RAG pipelines using Azure AI Search, vector databases, embeddings, semantic/hybrid search, and re-ranking strategies. * Develop secure conversational AI and multi-agent solutions using frameworks such as: Semantic Kernel, AutoGen, LangChain, CrewAI, Microsoft Agent Framework. * Build and integrate Model Context Protocol (MCP) services with governance, RBAC, audit logging, and secure tool-calling capabilities. * Develop scalable ingestion, ETL/ELT, and vectorization pipelines using Azure and AWS data platforms. * Work with Azure AI Agent Service and cloud-native AI infrastructure across Azure and AWS ecosystems. * Optimize LLM performance, latency, safety, and operational cost through evaluation frameworks and monitoring. * Implement CI/CD pipelines, automated testing, observability, and security best practices for AI workloads. * Collaborate with cross-functional teams including engineering, product, security, and platform teams. ## Related Videos - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [Azure-Well Architected Framework - designing mission critical workloads in practice](https://www.wearedevelopers.com/videos/1529-azure-well-architected-framework-designing-mission-critical-workloads-in-practice) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes šŸ§¬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [From A2A to MCP: How AIā€™s ā€œBrainsā€ are Connecting to ā€œArms and Legsā€](https://www.wearedevelopers.com/videos/1631-from-a2a-to-mcp-how-ai-s-brains-are-connecting-to-arms-and-legs) ## Related Articles - [Got AI ideas but no money? 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