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Overview\nWe are seeking a Senior AI Platform Engineer with overall 7 to 10 years of experience to design build and operate a secure scalable enterprise AI platform on Microsoft Azure primarily hosted on Azure Kubernetes Service AKS\nThe role requires a strong platform engineering mindset combining AI engineering cloudnative engineering DevSecOps LLMOps security automation observability and operational excellence The engineer will create reusable platform capabilities that enable teams to build and productionize generative AI RAG and agentic AI solutions efficiently\nKey Responsibilities\nDesign build and operate scalable AI platform capabilities on Azure and AKS\nEngineer capabilities for LLM applications RAG solutions AI agents multiagent workflows AI gateways guardrails and model evaluation\nDevelop reusable platform services APIs deployment templates and reference implementations\nBuild agent harness capabilities covering orchestration tool integration memory context management state management policy enforcement tracing and exception handling\nEnable secure integration with enterprise tools and data sources using Model Context Protocol MCP APIs connectors and eventdriven patterns\nBuild reusable RAG pipelines covering document ingestion chunking embeddings vector indexing retrieval reranking grounding and citations\nImplement vector database and enterprise search capabilities using technologies such as Azure AI Search PostgreSQL with pgvector or equivalent platforms\nImplement centralized AI guardrails for prompt injection unsafe content sensitivedata exposure inappropriate tool execution and unauthorized access\nEstablish CICD and Infrastructure as Code practices using GitHub GitHub Actions Terraform and container technologies\nEmbed security testing container scanning dependency scanning policy validation AI evaluation and guardrail testing into delivery pipelines\nImplement endtoend observability for infrastructure applications LLM calls token consumption RAG pipelines agent workflows security events and costs\nApply SRE practices covering availability performance capacity incident response resilience disaster recovery and automated remediation\nProvide selfservice onboarding golden paths sample repositories documentation and operational runbooks for engineering teams\nCollaborate with architects AI engineers developers security risk data and operations teams to establish enterprise AI platform standards\nPrimary Skills\nMicrosoft Azure and Azure Kubernetes Service\nKubernetes Docker and container orchestration\nAzure OpenAI and Microsoft Foundry\nLarge Language Models and generative AI\nAgentic AI and multiagent systems\nAgent harness and orchestration engineering\nModel Context Protocol and tool integration\nRetrievalAugmented Generation\nEmbeddings vector search and vector databases\nAzure AI Search PostgreSQL and pgvector\nAI gateways model gateways and API Management\nAI guardrails content safety and responsible AI\nPrompt model RAG and agent evaluation\nLLMOps MLOps and AI observability\nGitHub GitHub Actions and DevSecOps\nTerraform Bicep and Infrastructure as Code\nEntra ID managed identity RBAC Key Vault and private networking\nAzure Monitor Application Insights OpenTelemetry Prometheus and Grafana\nPython and any other programming language like Java C\nLangChain LangGraph Semantic Kernel AutoGen or equivalent frameworks\nRequired Qualifications\nStrong experience in building an enterprise AI platform cloud engineering DevOps SRE or distributed systems\nPractical experience with LLMs RAG embeddings vector databases AI agents MCP guardrails and AI evaluation\nExperience with Azure AI Search Azure API Management Azure Container Registry Key Vault and Azure monitoring services\nExperience with LangChain LangGraph Semantic Kernel MCP servers agent registries tool catalogs or model gateways\nExperience implementing multitenant AI platforms with workload identity network data and cost isolation\nFamiliarity with AI governance privacy model risk management threat modeling and regulatory controls\nHandson experience designing and operating production workloads on Azure and AKS\nStrong knowledge of Kubernetes networking security scaling identity observability and workload isolation\nExperience building automated delivery pipelines using GitHub Actions and Infrastructure as Code\nStrong understanding of APIs microservices eventdriven integration authentication authorization and secure software development\nExperience operationalizing production platforms with defined availability performance security and support requirements\nProficiency in at least one modern programming language\nStrong troubleshooting documentation 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