SR. AI ENGINEER, SMAI

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. Responsibilities and Tasks Understand Business Problems and Define Intelligent Solutions Develop a strong understanding of Micron’s business priorities, operational processes, enterprise systems, and strategic objectives. Partner with product owners, domain experts, data scientists, engineers, and business stakeholders to translate complex business problems into scalable technical solutions. Convert functional and non-functional requirements into solution architectures, technical designs, data requirements, and executable delivery plans. Identify opportunities where advanced analytics, machine learning, Generative AI, intelligent automation, or agentic capabilities can deliver measurable business value. Communicate technical options, dependencies, risks, and trade-offs clearly to both technical and non-technical stakeholders. Architect Scalable and Intelligent Enterprise Solutions Design secure, reliable, maintainable, and scalable solutions that integrate enterprise data, cloud platforms, applications, services, and AI capabilities. Define end-to-end architecture covering data ingestion, transformation, storage, retrieval, processing, APIs, user experiences, model integration, and production operations. Select appropriate architectural patterns, cloud services, data platforms, models, frameworks, and integration approaches based on performance, security, operability, cost, and business fit. Design modular services, APIs, event-driven components, intelligent workflows, and reusable platform capabilities. Contribute to architecture and design reviews while establishing reusable engineering patterns, technical standards, and quality gates. Build Reliable Data and Knowledge Foundations Identify and integrate relevant structured, semi-structured, and unstructured data from enterprise systems, databases, APIs, files, events, and streaming sources. Design and develop scalable ingestion, transformation, orchestration, and processing workflows for varying data volumes and latency requirements. Implement suitable data models, storage structures, contracts, metadata, lineage, retention, archival, and deletion strategies. Establish and monitor data-quality objectives, including completeness, accuracy, consistency, freshness, traceability, and fitness for purpose. Prepare trusted datasets and knowledge sources for reporting, analytics, machine learning, and AI-enabled applications. Develop retrieval pipelines that include document ingestion, parsing, chunking, metadata enrichment, embeddings, indexing, vector search, relevance evaluation, grounding, and citation strategies. Develop Production-Ready Applications and Services Lead the end-to-end design, development, testing, deployment, enhancement, and support of cloud-native and AI-enabled applications. Develop secure, maintainable, and high-performing backend services, APIs, microservices, integration components, and intelligent workflows. Build responsive, accessible, and reusable user experiences using suitable modern application frameworks. Apply sound object-oriented design, domain-driven design, asynchronous processing, dependency injection, and distributed-system principles where appropriate. Integrate relational, non-relational, analytical, streaming, and vector data stores into enterprise applications. Modernize existing solutions and progressively evolve legacy systems toward modular, cloud-native, and AI-enabled architectures. Engineer AI-Enabled Capabilities Design and implement solutions using machine learning, Large Language Models, Generative AI, Retrieval-Augmented Generation, tool calling, copilots, and AI agents. Develop prompt orchestration, context-management, memory, reasoning, workflow, and multi-agent patterns appropriate to the business use case. Enable governed connectivity between intelligent agents, enterprise tools, APIs, applications, and data sources using Model Context Protocol or similar integration patterns. Establish systematic evaluation approaches for model quality, retrieval relevance, groundedness, response accuracy, latency, safety, and user experience. Implement safeguards to reduce hallucinations, unsupported responses, prompt injection, sensitive-data exposure, and unintended system actions. Design appropriate human-in-the-loop mechanisms, fallback behaviors, approval controls, and escalation paths for higher-risk scenarios. Evaluate models, frameworks, and emerging technologies through structured prototypes and recommend adoption based on measurable technical and business outcomes. Ensure Security, Responsible AI, and Governance Apply security-by-design and responsible AI principles throughout solution architecture, implementation, and operation. Implement appropriate authentication, authorization, role-based access controls, secrets management, encryption, network controls, and secure coding practices. Protect confidential and sensitive information through data classification, access restrictions, privacy controls, filtering, masking, and secure handling practices. Incorporate content safety, prompt-injection defenses, auditability, traceability, explainability, and human oversight based on the use case. Ensure that solutions comply with applicable enterprise architecture, security, privacy, data-governance, software-development, and AI-governance standards. Assess third-party libraries, models, dependencies, and services for security, licensing, operational, and supply-chain risks. Own Engineering Quality and Production Readiness Build automated unit, integration, system, regression, performance, security, and AI evaluation tests. Establish continuous integration and delivery pipelines with appropriate code-quality checks, automated validation, deployment controls, and rollback mechanisms. Implement logging, metrics, distributed tracing, alerting, health monitoring, and operational dashboards across solution components. Optimize solutions for reliability, scalability, availability, performance, latency, throughput, and cost. Participate in production support, incident response, troubleshooting, root-cause analysis, corrective actions, and continuous service improvement. Define and monitor service-level indicators, operational thresholds, capacity requirements, and production-readiness criteria. Create and maintain technical documentation, architecture decisions, data contracts, API specifications, support procedures, and operational runbooks. Provide Technical Leadership and Collaboration Lead technical discussions, architecture reviews, code reviews, design reviews, and engineering-quality assessments. Mentor associate and mid-level engineers through technical guidance, pairing, constructive feedback, and knowledge sharing. Promote reusable solutions, coding standards, automation, documentation, and engineering best practices across teams. Coordinate with globally distributed and cross-functional teams to manage dependencies, resolve technical challenges, and deliver incremental business value. Help teams make informed decisions by clearly articulating technical risks, alternatives, constraints, and long-term implications. Use AI-assisted engineering tools responsibly for analysis, coding, testing, documentation, and developer productivity while maintaining human review, accountability, and compliance. Minimum Qualifications Bachelor’s or Master’s degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Software Engineering, Computer Engineering, Mathematics, Statistics, or a related technical discipline. 5+ years of professional experience developing, delivering, and supporting production-grade enterprise solutions. Demonstrated