Sr AI Engineer, SMART MFG & AI
Micron · Posted Sep 28
DRAM, NAND, HBM memory and computer data storage solutions
Hyderabad, Telangana, IndiaFull-timeOnsiteSenior Level5–10 years₹35.0L–₹60.0L yearly100+ applicants
SemiconductorsHardwareComputer MemoryData StorageArtificial IntelligencePublic Company
Full time
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About the role
The Smart Manufacturing and Artificial Intelligence (SMAI) organization leads Micron’s AI-first transformation by building intelligent applications, AI-powered platforms, data products, and automation solutions across manufacturing, engineering, and business operations. The Senior AI Full Stack Engineer will lead the architecture, design, development, deployment, and operationalization of enterprise-grade AI-powered applications. The role involves building C#/.NET/ASP.NET Core backend services and frontend applications while integrating LLMs, AI agents, RAG, MCP, enterprise data platforms, and cloud services.
What you will do
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Skills used in this role
C#.NETASP.NET CoreRESTful APIsAngularReactBlazorVue.jsAzure OpenAIMicrosoft Semantic KernelMicrosoft CopilotLarge Language ModelsGenerative AIRetrieval-Augmented GenerationModel Context ProtocolSQLMicrosoft SQL ServerPostgreSQLAzureGCPAWSOpenShiftKubernetesDockerAzure DevOps
What the employer is looking for
- 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.
- 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.
Benefits and support
- Compensation and benefits are detailed in the job posting
About Micron
Micron Technology, Inc. is a world leader in innovative memory and storage solutions, manufacturing DRAM, NAND, NOR, and High Bandwidth Memory (HBM) technologies. The company provides foundational computing capabilities for artificial intelligence, 5G, data centers, and consumer electronics worldwide. Headquartered in Boise, Idaho, Micron plays a critical role in powering next-generation computing architectures.
- Industry
- Semiconductors
- Company size
- 56,000 employees
- Founded
- October 5, 1978
- Location
- Boise, Idaho, USA
- Funding stage
- Public Company
Leadership
SM
Sanjay Mehrotra
Chairman and Chief Executive Officer
MM
Mark Murphy
Executive Vice President and Chief Financial Officer
MB
Manish Bhatia
President and Chief Operating Officer
SJ
Scott J. DeBoer
President and Chief Technology and Products Officer
AA
April Arnzen
Executive Vice President and Chief People Officer
MR
Michael Ray
Senior Vice President, Chief Legal Officer and Corporate Secretary