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 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, 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. 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. Implement authentication, authorization, role-based access control, secrets management, encryption, and secure service communication. 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. Implement structured logging, metrics, distributed tracing, dashboards, alerts, and health checks. Define service-level indicators and operational thresholds appropriate to the application. Design applications with timeout, retry, circuit-breaker, rate-limiting, graceful-degradation, and failure-recovery mechanisms. 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. Develop operational documentation, troubleshooting guides, support procedures, and recovery instructions. Use production insights and user feedback to drive continuous product and platform improvements. Collaboration and Technical Leadership Partner with product owners and business stakeholders to understand problems, define outcomes, and refine requirements. Communicate architecture decisions, technical trade-offs, risks, dependencies, and delivery status clearly. Facilitate technical discussions, design reviews, code reviews, and problem-solving sessions. 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. Influence technical direction and engineering standards across teams without relying solely on formal authority. Collaborate effectively with globally distributed and cross-functional teams. Contribute to architecture communities, engineering forums, technical demonstrations, and internal knowledge-sharing initiatives. Minimum Qualifications Bachelor’s or Master’s degree in one of the following areas: Computer Science Information Technology Artificial Intelligence Data Science Software Engineering Computer Engineering A related technical discipline 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. Strong problem-solving, debugging, and analytical capabilities. Strong written and verbal communication skills. Ability to explain technical architecture, risks, and trade-offs to technical and non-technical stakeholders. Demonstrated ability to work independently while collaborating effectively within cross-functional teams. Required Technical Expertise Software Engineering Advanced proficiency in C# and .NET or 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 Blazor 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. Practical understanding of: Large Language Models Prompt engineering Retrieval-Augmented Generation Embeddings Vector search AI agents Tool and function calling Structured model outputs 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. Understanding of resiliency patterns, fault tolerance, retries, timeouts, circuit breakers, and graceful degradation. Experience participating in operational support, incident response, and root-cause analysis. Ability to use operational data to improve application reliability, performance, and user experience. Security Practical knowledge of authentication, authorization, role-based access control, and secure API development. 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. Preferred Qualifications Experience with the Microsoft Azure ecosystem, including one or more of the following: Azure OpenAI Azure AI Search Azure App Service Azure Functions Azure API Management 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. Knowledge of MLOps or LLMOps practices. 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. Senior-Level Competencies Technical Ownership Independently drives complex and ambiguous work across multiple technical components. 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. Architecture Judgment Makes informed decisions across scalability, security, reliability, cost, maintainability, and delivery speed. Selects technologies based on business needs and engineering evidence rather than technology trends alone. Recognizes when to build custom capabilities and when to use existing enterprise or managed services. Documents key decisions and communicates the rationale and trade-offs clearly. Engineering Excellence Raises software quality through automation, reviews, standards, testing, reusable components, and operational discipline. Identifies recurring engineering problems and develops reusable solutions. Promotes simple, maintainable designs and avoids unnecessary technical complexity. Uses measurable quality and operational indicators to guide improvements. Collaboration and Influence Builds alignment across software engineering, data science, architecture, platform, product, security, and business teams. Communicates technical topics clearly and adjusts communication based on the audience. Raises risks early and proposes practical mitigation options. Influences technical direction through expertise, evidence, trust, and constructive collaboration. Mentorship Mentors associate and mid-level engineers. Provides actionable feedback through code reviews, design reviews, and technical discussions. Helps other engineers build stronger design, coding, testing, debugging, and production-support capabilities. Creates opportunities for knowledge sharing and encourages independent problem-solving. Business Orientation Connects technical decisions to customer needs, business value, operational outcomes, and user experience. Prioritizes work based on value, risk, complexity, and delivery feasibility. Measures success based on solution adoption and effectiveness, not only feature completion. Balances experimentation with the need for secure, reliable, and sustainable enterprise solutions. Continuous Learning Tracks developments in software engineering, cloud computing, Generative AI, Agentic AI, and data platforms. Evaluates emerging tools and frameworks critically before recommending adoption. Converts useful technical advances into practical and governed enterprise solutions. Shares lessons learned and contributes to the continuous improvement of the wider engineering organization. Expected Outcomes The Senior AI Full Stack Engineer is expected to deliver: Reliable, secure, scalable, and maintainable AI-powered applications. Solutions that meet agreed functional and non-functional requirements. Clear technical designs, architecture decisions, and operational documentation. Production systems with appropriate testing, evaluation, monitoring, security, and support readiness. Reusable engineering patterns and components that improve team productivity. Improved engineering capability through mentoring, reviews, and knowledge sharing. 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. What We Look For Strong analytical thinking and a structured approach to solving complex and ambiguous problems. Deep technical ownership and accountability for production outcomes. Strong software-design, development, testing, and troubleshooting capabilities. Sound technical judgment and the ability to make balanced engineering decisions. Customer-focused communication and the ability to influence without relying on authority. Pragmatism in balancing innovation with security, reliability, maintainability, cost, and delivery constraints. Curiosity about emerging AI technologies, combined with healthy skepticism and disciplined validation. A continuous-improvement mindset and willingness to challenge inefficient engineering practices. Inclusive collaboration and a commitment to building a high-trust engineering culture. Ability to operate effectively in a fast-paced and highly collaborative environment. 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 MLOps and LLMOps Responsible AI and AI Governance Platform Engineering Software Architecture Engineering Leadership Technical Program Leadership Role Vision 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. This role is not limited to implementing application features. 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. Main Improvements Made Replaced associate-level language such as “learn,” “assist,” and “participate” with senior-level expectations such as “lead,” “design,” “own,” “define,” “influence,” and “mentor.” Added clear ownership of