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 system integration.

Strong knowledge of agent fundamentals including RAG, tool use, prompt engineering, prompt evaluation, memory/state design, multi-agent patterns, cost optimization, and guardrails.

Application Development Skills

Frontend development experience with React and TypeScript .

Experience integrating frontend applications with REST and GraphQL APIs.

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