Senior AI Engineer

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Job Summary:   We are   seeking   a technically proficient and proactive   Senior AI Engineer   to support, develop, and enhance cloud-based AI solutions, AI Agents, and custom applications across   Microsoft Azure, Amazon Web Services AWS, and Google Cloud Platform GCP . This role will   be responsible for   designing, developing, deploying, and supporting enterprise-grade AI applications, agentic solutions, RAG-based systems, automation workflows, and cloud-native integrations.   The ideal candidate should have strong hands-on experience in   AI Agent development, application development, multi-cloud AI platforms, backend and frontend engineering, DevOps, CI/CD, Infrastructure as Code, security, monitoring, and operational excellence . The role requires deep technical   expertise , strong troubleshooting skills, and the ability to collaborate with cloud, application, data, security, and platform teams to deliver scalable, secure, and reliable AI capabilities.   Key Responsibilities:   1. AI Agent Development and Engineering   Design, develop, and deploy AI Agents using cloud-native and open-source agent frameworks.   Build agentic workflows using   Azure AI Foundry Agent Service ,   AWS Bedrock   AgentCore ,   LangGraph ,   Strands Agents SDK ,   Semantic Kernel , and   AutoGen .   Implement   tool /function calling, memory, state management, checkpointing, multi-agent orchestration, and agent-to-agent workflows.   Integrate enterprise tools, APIs, databases, knowledge bases, and automation systems using   MCP Model Context Protocol   and custom connectors.   Design and implement   RAG Retrieval-Augmented Generation   solutions using vector stores, knowledge bases, and search services.   Develop guardrails, prompt evaluation, content safety controls, tracing, and observability for AI Agent solutions.   Optimize   AI Agent performance, cost, latency, reliability, and user experience.   2. Custom Application Development   Develop scalable web applications and internal tools to enable AI, automation, and cloud service capabilities.   Build frontend applications using   React ,   TypeScript , REST API integration,   GraphQL   integration, and secure authentication flows.   Implement authentication and authorization using   OAuth2 ,   OIDC , and   MSAL .   Develop backend services using   C#/.NET ,   Python   FastAPI , or   Node.js .   Design and build microservices, APIs, event-driven services, and cloud-native integrations.   Work with relational and NoSQL databases including   SQL ,   Cosmos DB , and   DynamoDB .   Integrate messaging and event-driven platforms such as   Azure Service Bus ,   AWS SQS , and   AWS SNS .   Implement backend testing, API testing, unit testing, integration testing, and code quality practices.   Ensure application solutions are secure, scalable, maintainable, and aligned with enterprise architecture standards.   3. Cloud AI Services Support   Deploy, manage, and support AI/ML workloads across Azure, AWS, and GCP.   Support   Azure AI Foundry ,   Azure OpenAI ,   Azure AI Search , model deployments, evaluation, tracing, tool/function calling, and content safety controls.   Support   AWS Bedrock ,   Bedrock Studio ,   Amazon Q Business ,   Bedrock Knowledge Bases ,   Guardrails , and   Bedrock   AgentCore .   Support   GCP Vertex AI ,   Vertex AI Search , and   Gemini Models .   Ensure secure and compliant deployment of AI services, APIs, agents, and applications.   4. Cloud Operations and Optimization   Resolve complex cloud infrastructure and AI platform issues across Azure, AWS, and GCP.   Perform root cause analysis for incidents related to AI services, agents, applications, APIs, integrations, and cloud platforms.   Manage and   optimize   cloud resources including   compute , storage, networking, databases, containers, and AI services.   Implement and   monitor   backup, disaster recovery, high availability, resiliency, and operational readiness.   Identify   automation opportunities to reduce manual effort and improve operational efficiency.   Optimize   cloud and AI workloads for cost, performance, security, and reliability.   5. Automation,   DevOps   and CI/CD   Develop automation using   PowerShell ,   Python ,   Terraform , and cloud-native tools.   Implement Infrastructure as Code using   Terraform   and   Bicep .   Design and maintain CI/CD pipelines using   GitHub Actions   and   Azure DevOps .   Automate build, test, security scan, deployment, and environment promotion workflows.   Support containerized deployments and cloud-native release patterns.   Integrate automated validation, testing, approval gates, and release controls.   Improve deployment reliability for AI Agents, APIs, applications, infrastructure, and cloud services.   6. Version Control and Engineering Practices   Use   Git   effectively for source control, branching, merging, pull requests, and code reviews.   Follow branching strategies such as   trunk-based development   or   GitFlow .   Manage code repositories in   GitHub ,   Azure Repos , or   GitLab .   Resolve merge conflicts and   maintain   clean,   reviewable   code   history.   Participate in PR reviews, enforce coding standards, and promote secure development practices.   Maintain reusable templates, libraries, automation scripts, and shared engineering assets.   7 . Observability, Reliability and Support   Implement monitoring, logging, tracing, and   alerting for   AI Agents, applications, APIs, and cloud services.   Support observability for AI platforms including agent traces, tool calls, model interactions, latency, failure rates, and usage trends.   Troubleshoot production issues involving AI workflows, integrations, authentication, service connectivity, and cloud resources.   Participate in rotational support or   on-call   activities as   required .   Contribute to operational readiness reviews, incident management, and continuous improvement.   Required Skills and Qualifications   Education   Bachelor’s degree in Computer Science , Information Technology, Engineering, or   a related   field.   Core Technical Skills   Hands-on experience with cloud-native AI services, model deployment, RAG implementation, and AI platform operations.   Strong programming and scripting skills in   Python ,   PowerShell , and at least one backend language such as   C#/.NET ,   Node.js , or   Python   FastAPI .   Experience building APIs, microservices, automation workflows, and production-grade integrations.   Experience with SQL and NoSQL databases including   Cosmos DB ,   DynamoDB , and relational databases.   Experience with vector stores, search platforms, knowledge bases, and enterprise data integration.   Strong understanding of DevOps, CI/CD,   IaC , containerized deployments, and release automation.   Solid understanding of cloud security, networking, identity, compliance, monitoring, and operational support.   AI and Agent Development Skills   Hands-on experience with   Azure AI Foundry Agent Service , model deployment, tool/function calling, RAG over Azure AI Search, evaluations, tracing, and content safety.   Hands-on experience or strong working knowledge of   AWS Bedrock   AgentCore , including Runtime, Gateway, Memory, Identity, observability, Bedrock models, Knowledge Bases, and Guardrails.   Experience with agent frameworks such as   LangGraph ,   Strands Agents SDK ,   Semantic Kernel , and   AutoGen .   Understanding of   MCP Model Context Protocol   for tool and