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

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.

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.

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 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.

Lead the end-to-end architecture, design, development, testing, deployment, and support of full-stack and AI-powered applications.

Own complex features, services, and platforms from initial design through production deployment and ongoing support.

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.

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.

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.

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.

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.

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.

Ensure AI-generated code is reviewed, validated, tested, and compliant with security and intellectual-property requirements.

Continuously improve engineering processes, automation, developer experience, and delivery efficiency.

Security and Responsible AI

Apply security-by-design principles throughout the software and AI development lifecycle.

Follow secure coding practices to reduce vulnerabilities in applications, APIs, dependencies, containers, and cloud configurations.

Perform threat analysis for AI applications, agent tools, APIs, data flows, and enterprise integrations.

Implement access restrictions and allowlists that prevent AI agents from using unauthorized tools or data sources.

Design controls for sensitive-data handling, privacy, content safety, auditability, and regulatory compliance.

Implement logging and traceability for agent actions, tool calls, data retrieval, user interactions, and model responses.

Apply responsible AI principles including fairness, transparency, safety, privacy, accountability, and human oversight.

Collaborate with cybersecurity, privacy, legal, compliance, and architecture teams when delivering sensitive or high-impact solutions.

Reliability and Production Operations

Own production readiness, release planning, deployment validation, and operational handover.

Monitor application reliability, AI response quality, latency, throughput, token consumption, infrastructure usage, and operating costs.

Troubleshoot complex issues across frontend, backend, API, database, AI, network, and cloud layers.

Participate in incident response, root-cause analysis, corrective actions, and preventive improvements.

Communicate architecture decisions, technical trade-offs, risks, dependencies, and delivery status clearly.

Mentor associate and mid-level engineers through technical guidance, pair programming, knowledge sharing, and constructive feedback.

Help engineers improve their software-design, development, testing, debugging, and operational-support capabilities.

Promote engineering consistency, documentation, reuse, automation, and continuous learning.

Contribute to architecture communities, engineering forums, technical demonstrations, and internal knowledge-sharing initiatives.

Bachelor’s or Master’s degree in one of the following areas:

Computer Science

Information Technology

Artificial Intelligence

Data Science

Software Engineering

Computer Engineering

Five or more years of professional software engineering experience.

Demonstrated experience delivering production-grade enterprise applications.

Experience owning complex features, services, or applications from design through production support.

Ability to explain technical architecture, risks, and trade-offs to technical and non-technical stakeholders.

Software Engineering

NET or ASP.NET Core. Strong knowledge of object-oriented programming and software-design principles.

Experience with RESTful APIs, microservices, dependency injection, asynchronous programming, and background services.

Strong understanding of data structures, algorithms, design patterns, and application architecture.

Experience building secure, scalable, testable, and maintainable enterprise software.

Experience with unit testing, integration testing, mocking frameworks, and automated quality validation.

Ability to diagnose and resolve complex application and performance problems.

Full-Stack Development

Production experience with at least one modern frontend framework, such as:

Angular

React

Vue.js

Strong understanding of HTML, CSS, TypeScript, JavaScript, web security, and client-server architecture.

Experience integrating frontend applications with REST APIs and enterprise authentication systems.

Understanding of responsive design, accessibility, performance optimization, and reusable component development.

AI Engineering

Hands-on experience integrating AI or machine-learning capabilities into enterprise applications.

Large Language Models

AI agents

Tool and function calling

AI evaluation

Responsible AI

Experience designing grounded AI solutions using enterprise documents and data.

Understanding of common LLM application risks, including hallucination, prompt injection, unauthorized data access, and sensitive-data exposure.

Ability to evaluate AI solutions based on accuracy, relevance, groundedness, latency, cost, safety, and maintainability.

Data Platforms

Strong SQL development and query-optimization skills.

Experience with relational databases such as:

Microsoft SQL Server

PostgreSQL

Equivalent enterprise relational databases

Working knowledge of non-relational databases, document stores, caching platforms, or vector databases.

Understanding of data modeling, schema design, indexing, data quality, and data-integration patterns.

Experience consuming or developing data pipelines that support enterprise applications and AI workloads.

Cloud and Containers

Experience developing and deploying cloud-native solutions on Azure, GCP, AWS, or an equivalent enterprise cloud platform.

Experience with Docker and containerized application development.

Experience deploying or supporting applications on Kubernetes or OpenShift.

Understanding of cloud identity, network connectivity, configuration, storage, secrets, managed services, and application hosting.

Experience designing applications for scalability, reliability, availability, and recoverability.

DevOps and Software Quality

Strong experience with Git and collaborative software-development workflows.

Experience with pull requests, branching strategies, release management, and version control.

Experience implementing CI/CD pipelines using Azure DevOps, GitHub Actions, or equivalent tools.

Experience integrating automated testing, code-quality validation, security scanning, and deployment controls into delivery pipelines.





Understanding of software-development lifecycle and Agile delivery practices.

Production Operations

Experience with application logging, metrics, dashboards, alerting, and distributed tracing.

Experience troubleshooting production applications across multiple technical layers.

Experience participating in operational support, incident response, and root-cause analysis.

Ability to use operational data to improve application reliability, performance, and user experience.

Experience with enterprise identity providers, OAuth, OpenID Connect, tokens, certificates, or equivalent authentication mechanisms.

Understanding of secure secrets management, encryption, data protection, and dependency risk.

Familiarity with secure software-development practices and common application-security vulnerabilities.

Understanding of privacy, data-governance, and responsible-AI considerations for enterprise applications.

Experience with the Microsoft Azure ecosystem, including one or more of the following:

Azure AI Search

Azure DevOps

Microsoft Semantic Kernel

Microsoft Copilot technologies

Experience designing or delivering Agentic AI applications.

Experience implementing Model Context Protocol integrations or similar tool-integration frameworks.

Experience with multi-agent orchestration, enterprise copilots, workflow automation, or human-in-the-loop AI systems.

Proficiency in Python and commonly used AI or data libraries.

Experience with Java, Scala, PySpark, or distributed data-processing technologies.

Experience with vector databases, knowledge graphs, event-driven architectures, message brokers, or stream-processing platforms.

Experience with infrastructure as code and automated environment provisioning.

Experience with Kubernetes operators, service meshes, platform engineering, or internal developer platforms.

Experience with model, prompt, configuration, dataset, and evaluation versioning.

Experience implementing AI-quality monitoring and production evaluation pipelines.

Experience with computer vision, image analytics, video analytics, or industrial AI solutions.

Understanding of semiconductor manufacturing, smart manufacturing, industrial systems, factory automation, or high-volume operational environments.

Contributions to reusable technical platforms, engineering standards, architecture communities, developer tooling, or open-source projects.

Takes accountability for technical quality, delivery, operational readiness, and production outcomes.

Identifies technical risks and dependencies before they become delivery issues.

Follows through on commitments and ensures solutions remain maintainable after deployment.

Selects technologies based on business needs and engineering evidence rather than technology trends alone.

Raises software quality through automation, reviews, standards, testing, reusable components, and operational discipline.

Uses measurable quality and operational indicators to guide improvements.

Builds alignment across software engineering, data science, architecture, platform, product, security, and business teams.

Raises risks early and proposes practical mitigation options.

Mentors associate and mid-level engineers.

Helps other engineers build stronger design, coding, testing, debugging, and production-support capabilities.

Connects technical decisions to customer needs, business value, operational outcomes, and user experience.

Prioritizes work based on value, risk, complexity, and delivery feasibility.

Tracks developments in software engineering, cloud computing, Generative AI, Agentic AI, and data platforms.

The Senior AI Full Stack Engineer is expected to deliver:

Reliable, secure, scalable, and maintainable AI-powered applications.

Production systems with appropriate testing, evaluation, monitoring, security, and support readiness.

Evidence-based and cost-aware technology recommendations.

Reduced production risk through strong engineering and operational practices.

Measurable improvements in customer experience, engineering productivity, manufacturing operations, or business outcomes.

Strong software-design, development, testing, and troubleshooting capabilities.

Pragmatism in balancing innovation with security, reliability, maintainability, cost, and delivery constraints.

Curiosity about emerging AI technologies, combined with healthy skepticism and disciplined validation.

Inclusive collaboration and a commitment to building a high-trust engineering culture.

Career Growth Opportunities

As a Senior AI Full Stack Engineer, you will have opportunities to grow in areas including:

AI Full Stack Engineering

Generative AI Engineering

Agentic AI Architecture

AI Platform Engineering

Cloud Solution Architecture

Data and Integration Architecture

Responsible AI and AI Governance

Platform Engineering

Software Architecture

Senior AI Full Stack Engineers help shape the next generation of AI-native engineering at Micron.

They combine software engineering, cloud computing, data engineering, and Artificial Intelligence to create secure, governed, scalable, and production-ready solutions that transform how Micron engineers, manufactures, and innovates.

It provides technical leadership across solution architecture, engineering quality, AI integration, production operations, mentoring, and continuous improvement.

Join us in shaping the future of AI-powered Smart Manufacturing and Digital Transformation at Micron .

Replaced associate-level language such as “learn,” “assist,” and “participate” with senior-level expectations such as “lead,” “design,” “own,” “define,” “influence,” and “mentor.”

Expanded Generative AI requirements to cover RAG, agents, MCP, evaluation, grounding, safeguards, and responsible AI.

Defined measurable outcomes that distinguish a senior engineer from an experienced individual contributor focused only on implementation.

Below is the complete, copy-ready content for a Senior AI Full Stack Engineer position requiring 5+ years of experience . It upgrades the associate-level profile by adding architecture ownership, production AI engineering, technical leadership, mentoring, security, reliability, and operational accountability.

Senior AI Full Stack Engineer, SMAI

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.

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.

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 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.

Lead the end-to-end architecture, design, development, testing, deployment, and support of full-stack and AI-powered applications.

Own complex features, services, and platforms from initial design through production deployment and ongoing support.

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 .

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.

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.

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.

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.

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.

Ensure AI-generated code is reviewed, validated, tested, and compliant with security and intellectual-property requirements.

Continuously improve engineering processes, automation, developer experience, and delivery efficiency.

Security and Responsible AI

Apply security-by-design principles throughout the software and AI development lifecycle.

Follow secure coding practices to reduce vulnerabilities in applications, APIs, dependencies, containers, and cloud configurations.

Perform threat analysis for AI applications, agent tools, APIs, data flows, and enterprise integrations.

Implement access restrictions and allowlists that prevent AI agents from using unauthorized tools or data sources.

Design controls for sensitive-data handling, privacy, content safety, auditability, and regulatory compliance.

Implement logging and traceability for agent actions, tool calls, data retrieval, user interactions, and model responses.

Apply responsible AI principles including fairness, transparency, safety, privacy, accountability, and human oversight.

Collaborate with cybersecurity, privacy, legal, compliance, and architecture teams when delivering sensitive or high-impact solutions.

Reliability and Production Operations

Own production readiness, release planning, deployment validation, and operational handover.

Monitor application reliability, AI response quality, latency, throughput, token consumption, infrastructure usage, and operating costs.

Troubleshoot complex issues across frontend, backend, API, database, AI, network, and cloud layers.

Participate in incident response, root-cause analysis, corrective actions, and preventive improvements.

Communicate architecture decisions, technical trade-offs, risks, dependencies, and delivery status clearly.

Mentor associate and mid-level engineers through technical guidance, pair programming, knowledge sharing, and constructive feedback.

Help engineers improve their software-design, development, testing, debugging, and operational-support capabilities.

Promote engineering consistency, documentation, reuse, automation, and continuous learning.

Contribute to architecture communities, engineering forums, technical demonstrations, and internal knowledge-sharing initiatives.

Bachelor’s or Master’s degree in one of the following areas:

Computer Science

Information Technology

Artificial Intelligence

Data Science

Software Engineering

Computer Engineering

Five or more years of professional software engineering experience.

Demonstrated experience delivering production-grade enterprise applications.

Experience owning complex features, services, or applications from design through production support.

Ability to explain technical architecture, risks, and trade-offs to technical and non-technical stakeholders.

Software Engineering

NET or ASP.NET Core .

Strong knowledge of object-oriented programming and software-design principles.

Experience with RESTful APIs, microservices, dependency injection, asynchronous programming, and background services.

Strong understanding of data structures, algorithms, design patterns, and application architecture.

Experience building secure, scalable, testable, and maintainable enterprise software.

Experience with unit testing, integration testing, mocking frameworks, and automated quality validation.

Ability to diagnose and resolve complex application and performance problems.

Full-Stack Development

Production experience with at least one modern frontend framework, such as:

Angular

React

Vue.js

Strong understanding of HTML, CSS, TypeScript, JavaScript, web security, and client-server architecture.

Experience integrating frontend applications with REST APIs and enterprise authentication systems.

Understanding of responsive design, accessibility, performance optimization, and reusable component development.

AI Engineering

Hands-on experience integrating AI or machine-learning capabilities into enterprise applications.

Large Language Models

AI agents

Tool and function calling

AI evaluation

Responsible AI

Experience designing grounded AI solutions using enterprise documents and data.

Understanding of common LLM application risks, including hallucination, prompt injection, unauthorized data access, and sensitive-data exposure.

Ability to evaluate AI solutions based on accuracy, relevance, groundedness, latency, cost, safety, and maintainability.

Data Platforms

Strong SQL development and query-optimization skills.

Experience with relational databases such as:

Microsoft SQL Server

PostgreSQL

Equivalent enterprise relational databases

Working knowledge of non-relational databases, document stores, caching platforms, or vector databases.

Understanding of data modeling, schema design, indexing, data quality, and data-integration patterns.

Experience consuming or developing data pipelines that support enterprise applications and AI workloads.

Cloud and Containers

Experience developing and deploying cloud-native solutions on Azure, GCP, AWS, or an equivalent enterprise cloud platform.

Experience with Docker and containerized application development.

Experience deploying or supporting applications on Kubernetes or OpenShift.

Understanding of cloud identity, network connectivity, configuration, storage, secrets, managed services, and application hosting.

Experience designing applications for scalability, reliability, availability, and recoverability.

DevOps and Software Quality

Solid experience with Git and collaborative software-development workflows.

Experience with pull requests, branching strategies, release management, and version control.

Experience implementing CI/CD pipelines using Azure DevOps, GitHub Actions, or equivalent tools.

Experience integrating automated testing, code-quality validation, security scanning, and deployment controls into delivery pipelines.

Understanding of software-development lifecycle and Agile delivery practices.

Production Operations

Experience with application logging, metrics, dashboards, alerting, and distributed tracing.

Experience troubleshooting production applications across multiple technical layers.

Experience participating in operational support, incident response, and root-cause analysis.

Ability to use operational data to improve application reliability, performance, and user experience.

Experience with enterprise identity providers, OAuth, OpenID Connect, tokens, certificates, or equivalent authentication mechanisms.

Understanding of secure secrets management, encryption, data protection, and dependency risk.

Familiarity with secure software-development practices and common application-security vulnerabilities.

Understanding of privacy, data-governance, and responsible-AI considerations for enterprise applications.

Experience with the Microsoft Azure ecosystem, including one or more of the following:

Azure AI Search

Azure DevOps

Microsoft Semantic Kernel

Microsoft Copilot technologies

Experience designing or delivering Agentic AI applications.

Experience implementing Model Context Protocol integrations or similar tool-integration frameworks.

Experience with multi-agent orchestration, enterprise copilots, workflow automation, or human-in-the-loop AI systems.

Proficiency in Python and commonly used AI or data libraries.

Experience with Java, Scala, PySpark, or distributed data-processing technologies.

Experience with vector databases, knowledge graphs, event-driven architectures, message brokers, or stream-processing platforms.

Experience with infrastructure as code and automated environment provisioning.

Experience with Kubernetes operators, service meshes, platform engineering, or internal developer platforms.

Experience with model, prompt, configuration, dataset, and evaluation versioning.

Experience implementing AI-quality monitoring and production evaluation pipelines.

Experience with computer vision, image analytics, video analytics, or industrial AI solutions.

Understanding of semiconductor manufacturing, smart manufacturing, industrial systems, factory automation, or high-volume operational environments.

Contributions to reusable technical platforms, engineering standards, architecture communities, developer tooling, or open-source projects.

Takes accountability for technical quality, delivery, operational readiness, and production outcomes.

Identifies technical risks and dependencies before they become delivery issues.

Follows through on commitments and ensures solutions remain maintainable after deployment.

Selects technologies based on business needs and engineering evidence rather than technology trends alone.

Raises software quality through automation, reviews, standards, testing, reusable components, and operational discipline.

Uses measurable quality and operational indicators to guide improvements.

Builds alignment across software engineering, data science, architecture, platform, product, security, and business teams.

Raises risks early and proposes practical mitigation options.

Mentors associate and mid-level engineers.

Helps other engineers build stronger design, coding, testing, debugging, and production-support capabilities.

Connects technical decisions to customer needs, business value, operational outcomes, and user experience.

Prioritizes work based on value, risk, complexity, and delivery feasibility.

Tracks developments in software engineering, cloud computing, Generative AI, Agentic AI, and data platforms.

The Senior AI Full Stack Engineer is expected to deliver:

Reliable, secure, scalable, and maintainable AI-powered applications.

Production systems with appropriate testing, evaluation, monitoring, security, and support readiness.

Evidence-based and cost-aware technology recommendations.

Reduced production risk through strong engineering and operational practices.

Measurable improvements in customer experience, engineering productivity, manufacturing operations, or business outcomes.

Strong software-design, development, testing, and troubleshooting capabilities.

Pragmatism in balancing innovation with security, reliability, maintainability, cost, and delivery constraints.

Curiosity about emerging AI technologies, combined with healthy skepticism and disciplined validation.

Inclusive collaboration and a commitment to building a high-trust engineering culture.

Career Growth Opportunities

As a Senior AI Full Stack Engineer, you will have opportunities to grow in areas including:

AI Full Stack Engineering

Generative AI Engineering

Agentic AI Architecture

AI Platform Engineering

Cloud Solution Architecture

Data and Integration Architecture

Responsible AI and AI Governance

Platform Engineering

Software Architecture

Senior AI Full Stack Engineers help shape the next generation of AI-native engineering at Micron.

They combine software engineering, cloud computing, data engineering, and Artificial Intelligence to create secure, governed, scalable, and production-ready solutions that transform how Micron engineers, manufactures, and innovates.

It provides technical leadership across solution architecture, engineering quality, AI integration, production operations, mentoring, and continuous improvement.

Join us in shaping the future of AI-powered Smart Manufacturing and Digital Transformation at Micron .

About Micron Technology, Inc. With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM, NAND, and NOR memory and storage products through our Micron® and Crucial® brands. Every day, the innovations that our people create fuel the data economy, enabling advances in artificial intelligence and 5G applications that unleash opportunities — from the data center to the intelligent edge and across the client and mobile user experience.

To learn more, please visit micron.All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

To request assistance with the application process and/or for reasonable accommodations, please contact [View email address on joinrs.com Prohibits the use of child labor and complies with all applicable laws, rules, regulations, and other international and industry labor standards.

Micron does not charge candidates any recruitment fees or unlawfully collect any other payment from candidates as consideration for their employment with Micron.

AI alert: Candidates are encouraged to use AI tools to enhance their resume and/or application materials. However, all information provided must be accurate and reflect the candidate's true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in immediate disqualification.

Fraud alert: Micron advises job seekers to be cautious of unsolicited job offers and to verify the authenticity of any communication claiming to be from Micron by checking the official Micron careers website in the About Micron Technology, Inc.

📌 (Senior) AI DevOps Engineer (m/w/d)
🏢 Micron
📍 Hyderabad