Sr AI Engineer, SMART MFG & AI

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.
Senior AI Full Stack Engineer, SMAI
Our Vision
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.
About Our Team
The Smart Manufacturing and Artificial Intelligence (SMAI) organization is leading Micron’s AI-first transformation by building intelligent applications, AI-powered platforms, data products, and automation solutions that accelerate innovation across manufacturing, engineering, and business operations.
We are looking for an experienced and highly motivated Senior AI Full Stack Engineer who combines strong software engineering expertise with practical experience in Artificial Intelligence, cloud-native application development, data integration, and enterprise solution delivery.
In this role, you will work with engineers, data scientists, architects, product owners, and business stakeholders to design and deliver secure, scalable, and production-ready AI solutions that create measurable business value across Micron.
Position Overview
As a Senior AI Full Stack Engineer, you will lead the architecture, design, development, deployment, and operationalization of enterprise-grade AI-powered applications.
You will combine deep software engineering expertise with hands-on experience in Generative AI, Agentic AI, cloud-native engineering, data platforms, APIs, and modern user experiences. You will independently own complex technical outcomes, influence architecture decisions, guide engineering practices, mentor junior engineers, and collaborate with cross-functional teams to deliver reliable and maintainable solutions.
You will build enterprise-grade backend services and APIs using C# and .NET with ASP.NET Core, develop modern frontend applications, and integrate technologies such as Large Language Models, AI agents, Retrieval-Augmented Generation, Model Context Protocol, enterprise data platforms, and cloud services.
You will also establish engineering practices that improve software quality, security, reliability, observability, development velocity, and production support readiness.
Key Responsibilities
Solution Architecture and Technical Ownership
Lead the end-to-end architecture, design, development, testing, deployment, and support of full-stack and AI-powered applications.
Translate complex business requirements and non-functional requirements into scalable solution architectures, technical designs, and executable delivery plans.
Own complex features, services, and platforms from initial design through production deployment and ongoing support.
Evaluate technical alternatives and make balanced architecture decisions considering scalability, reliability, security, cost, maintainability, and delivery timelines.
Create and maintain solution architecture documents, technical specifications, API contracts, data flows, and architecture decision records.
Identify technical risks, dependencies, assumptions, and constraints early in the development lifecycle.
Drive technical alignment across engineering, architecture, data science, platform, security, and product teams.
Full-Stack Application Development
Design and develop secure, scalable, and maintainable backend services using C#, .NET, ASP.NET Core, and RESTful APIs.
Apply object-oriented design, dependency injection, asynchronous programming, domain-driven design, and clean architecture principles where appropriate.
Develop responsive, accessible, and reusable frontend components using frameworks such as Angular, React, Blazor, or Vue.js.
Design and implement reusable APIs, microservices, event-driven services, and enterprise integration components.
Implement secure authentication and authorization patterns for frontend, backend, API, and service-to-service communication.
Optimize application performance, API responsiveness, database access, and resource utilization.
Create reusable libraries, frameworks, templates, and engineering accelerators that improve development consistency and productivity.
Integrate enterprise systems, data platforms, cloud services, third-party services, and manufacturing applications.
Generative AI and Agentic AI Engineering
Design and implement applications that integrate Large Language Models and Generative AI capabilities.
Build enterprise solutions using technologies such as Azure OpenAI, Microsoft Semantic Kernel, Microsoft Copilot, or equivalent AI platforms.
Develop Retrieval-Augmented Generation solutions that combine LLMs with enterprise knowledge and data sources.
Design document ingestion and retrieval pipelines, including document parsing, chunking, metadata extraction, embeddings, vector indexing, semantic search, reranking, and grounded response generation.
Develop prompt templates, system instructions, tool definitions, function-calling integrations, and structured response mechanisms.
Build AI agents and copilots that securely interact with enterprise APIs, tools, workflows, databases, and applications.
Implement Model Context Protocol or similar integration patterns that enable governed connectivity between AI agents, enterprise tools, and data sources.
Design agentic workflows covering planning, tool selection, task execution, validation, exception handling, and human review.
Implement AI evaluation approaches for response quality, relevance, groundedness, accuracy, safety, latency, and cost.
Establish prompt, model, configuration, and evaluation versioning practices.
Develop safeguards against prompt injection, unauthorized tool access, sensitive-data exposure, hallucination, and unsafe model behavior.
Apply appropriate human-in-the-loop controls for high-impact or sensitive AI use cases.
Evaluate emerging AI frameworks, models, and tools through structured prototypes and technical assessments.
Data Engineering and Integration
Design and integrate relational, non-relational, analytical, streaming, and vector data sources.
Develop reliable data pipelines that support AI applications, analytical workloads, and enterprise integrations.
Apply strong SQL and data-modeling practices to support application and AI use cases.
Integrate databases such as Microsoft SQL Server, PostgreSQL, and other enterprise data platforms.
Work with non-relational databases, document stores, cache technologies, vector databases, and cloud-managed data services.
Define and enforce data contracts, validation rules, lineage expectations, and data-quality controls.
Optimize data access patterns, queries, indexes, caching approaches, and retrieval performance.
Collaborate with data engineers and data scientists to operationalize data and model workflows.
Ensure appropriate access controls, data classification, retention, privacy, and governance requirements are incorporated into solution designs.
Cloud-Native and Platform Engineering
Design and deploy enterprise applications on Azure, GCP, AWS, OpenShift, Kubernetes, or equivalent cloud and container platforms.
Build containerized applications using Docker and deploy them through Kubernetes or OpenShift.
Design applications for scalability, resiliency, availability, recoverability, and efficient resource utilization.
Use managed cloud services where appropriate for application hosting, AI integration, messaging, data processing, storage, monitoring, and security.
Configure application environments, secrets, certificates, identity access, network connectivity, and runtime settings.
Contribute to infrastructure-as-code and configuration-management practices.
Partner with platform, cloud, infrastructure, and security teams to ensure solutions comply with enterprise architecture and operational standards.
Analyze application usage and cloud-resource consumption to identify performance and cost-optimization opportunities.
DevOps and Engineering Excellence
Design and maintain continuous integration and continuous deployment pipelines using Azure DevOps, GitHub Actions, or equivalent tools.
Establish automated validation for builds, tests, security checks, dependency scanning, code quality, and deployment readiness.
Implement automated unit, integration, API, contract, performance, and end-to-end testing.
Define and enforce coding standards, branching strategies, pull-request practices, versioning approaches, and release controls.
Conduct detailed code reviews and provide constructive technical feedback.
Establish reusable engineering patterns, reference implementations, development templates, and quality gates.
Use AI-assisted engineering tools such as GitHub Copilot responsibly for coding, testing, documentation, and productivity improvement.
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