Top Artificial Intelligence Jobs in Hyderabad

Leads software engineering teams responsible for enterprise application development, modernization, cloud migration, platform transformation, and operational excellence. Sets technical strategy, architecture, engineering standards, and delivery practices while partnering with product, security, architecture, SRE, and business stakeholders. Manages talent development, vendors, budgets, resources, risks, and delivery metrics. Drives adoption of cloud-native technologies, DevSecOps, automation, observability, AI-enabled solutions, and responsible AI governance.

Design and validate high-speed electrical interfaces and package interconnects for memory products. Responsibilities include electrical model extraction, frequency- and time-domain simulation, test planning, measurement validation, package correlation, and collaboration with circuit, product, and system engineering teams. The role also develops scalable AI and automation solutions for engineering problems.

Design and validate high-speed package interfaces through electrical modeling, simulation, characterization, and testing. Collaborate with circuit, product, and system engineering teams to optimize silicon and package designs, correlate models with system measurements, and support future memory generations. Develop scalable AI and automation solutions for engineering challenges.

Develop and implement smart manufacturing and process control solutions for semiconductor assembly. Responsibilities include data analysis, process modeling, machine learning, closed-loop Run-to-Run control, system integration, requirements definition, technical support, mentorship, and training. The role collaborates with process teams to improve manufacturing capability, quality, and efficiency, while supporting network-wide implementation and AI-enabled predictive systems.

Develop centralized semiconductor quality screening and lot-disposition systems, assess wafer quality and reliability risks, analyze manufacturing data, and create statistical monitoring and automation solutions. The role supports defect containment, CAPA, global quality standardization, dashboards, AI and machine-learning initiatives, audits, and continuous improvement across Micron’s worldwide wafer-fabrication network.

Owns RTL architecture and design for on-chip memory controllers and complex memory-product blocks. Responsibilities include meeting performance, power, and area targets; developing synthesis and timing constraints; performing verification, timing closure, physical signoff, and ECOs; supporting post-layout simulation, silicon validation, and tape-out. The role also establishes design practices, mentors and trains engineers, participates in recruiting, collaborates across engineering teams, and applies AI to design and verification automation.

Leads global semiconductor defense-line process control systems, standardizing DL4 workflows, specifications, signal behavior, test coverage, failure analysis, and dispositions across sites. Drives automation and AI enhancements, monitors assembly and test specifications, reviews test programs, and improves detection and reliability processes. Requires expertise in semiconductor device physics, TCAD and compact modeling, analog circuit analysis, electrical test data analytics, root cause analysis, statistical modeling, and cross-functional collaboration.

Leads software engineering within an agile team, designing, developing, testing, troubleshooting, and maintaining secure, scalable production systems. Reviews code, improves operational stability, automates recurring remediation, and promotes enterprise-approved AI-assisted development practices. Coaches engineers on validating AI outputs for security, performance, and correctness. Evaluates vendor and internal architectural solutions and applies cloud-native, resilient, and secure software development practices using Java, Spring Boot, and React.

Leads hands-on development of enterprise Java applications, including architecture, coding, testing, deployment, troubleshooting, and production support. Designs microservices, REST APIs, event-driven systems, and modern web experiences while driving scalable, secure, highly available solutions. Applies Generative AI and AI-assisted development tools, including LLM integrations, RAG, vector databases, and AI agents. Mentors engineers, conducts technical and code reviews, promotes CI/CD and DevSecOps practices, and evaluates emerging technologies.

Leads and coaches multiple software engineering teams while overseeing resources, budgets, technical execution, and operating practices. Drives enterprise adoption of AI-assisted engineering and SDLC automation with governance, security, quality, and measurable delivery outcomes. Guides Java backend development and AWS cloud-native distributed services, participates in architecture and design reviews, promotes testing, CI/CD, observability, and secure coding, and collaborates with product and business stakeholders.

Leads the design, development, troubleshooting, and delivery of secure, scalable distributed systems. Builds Java and Spring Boot microservices, Kafka-based event architectures, AWS data platforms, and Spark processing solutions. Creates architecture artifacts, maintains production code, improves system performance, and analyzes large datasets. Promotes responsible use of AI-assisted engineering tools for coding, testing, documentation, troubleshooting, and code review while coaching teams on secure validation practices.

Leads software engineering for core infrastructure platforms, designing, developing, testing, debugging, and operating secure, scalable production systems. Drives adoption of approved AI-assisted development practices, establishes validation standards for AI outputs, automates recurring remediation, improves application resiliency, and evaluates architectural solutions with vendors and internal teams. Requires advanced programming skills, strong SDLC and agile expertise, cloud-native experience, and financial services IT knowledge.

Build and ship production-ready enterprise software using AI throughout the engineering lifecycle. Develop scalable Jakarta EE applications, APIs, and microservices; integrate AI components; and manage testing, deployment, monitoring, quality, and compliance. Lead AI-native development best practices, make team decisions, mentor engineers, and coordinate cross-functional collaboration. The role requires five or more years of Jakarta EE experience and 15 years of full-time education.

Design, develop, and maintain OneStream and EPM solutions, including architecture, infrastructure, security compliance, governance, integrations, automation, and testing. Translate business requirements into technical solutions, integrate OneStream with enterprise systems, troubleshoot complex issues, assess operational improvements, apply AI technologies, and collaborate with product, QA, and operations teams. Mentor junior engineers and promote customer-focused software engineering practices.

Manages solution architecture teams delivering complex Microsoft Cloud and AI transformation engagements. Oversees customer and partner alignment, architecture design, requirements traceability, testing, governance, risk mitigation, delivery quality, and portfolio outcomes. Coaches architects, supports presales and strategic account growth, resolves escalations, develops reusable intellectual property, and serves as a trusted technical advisor to senior stakeholders. Builds thought leadership through mentoring, knowledge sharing, industry engagement, and external presentations.

Leads the design, development, deployment, and operation of large-scale Kubernetes platforms and distributed systems. Owns complex technical initiatives, develops operators and controllers in Go, improves reliability and observability, and works across cloud infrastructure, automation, stateful workloads, and microservices. Partners with senior architects, resolves technical risks, mentors engineers, and drives platform strategy through design reviews and cross-functional collaboration.

Leads the design, development, deployment, and operation of large-scale cloud-native platform services. Builds and maintains Kubernetes operators, controllers, infrastructure automation, and distributed systems while ensuring reliability, scalability, observability, and security. Owns complex technical initiatives end to end, partners with senior architects, resolves technical risks, and mentors engineers. The role also explores AI-powered tooling, agentic operations, and AI integration into infrastructure engineering.

Develops automated testing frameworks and validation strategies for applications, APIs, microservices, cloud platforms, and AI systems. Designs functional, integration, regression, and end-to-end tests; evaluates generative AI, LLM, RAG, and agentic workflows for accuracy, groundedness, hallucinations, relevance, and latency. Collaborates with developers, architects, AI engineers, and DevOps teams to improve release quality and CI/CD automation.

Develop and maintain automated testing frameworks and validation strategies for applications, APIs, microservices, cloud-native systems, and AI solutions. Design evaluations for Generative AI, LLM, RAG, and agentic systems, measuring accuracy, relevance, groundedness, hallucinations, response quality, and latency. Collaborate with engineering and DevOps teams to improve test coverage, release confidence, and CI/CD quality.

Designs and maintains automated testing frameworks and validation strategies across UI, API, backend, integration, and end-to-end applications. Tests traditional software and AI systems, including Generative AI, LLM, RAG, and agentic workflows. Develops benchmark datasets and evaluation pipelines, validates accuracy, groundedness, relevance, hallucinations, latency, and response quality, and supports Agile, DevOps, cloud-native, and CI/CD delivery teams.

Leads the design and operation of scalable backend services, telemetry systems, data pipelines, and diagnostic workflows for Windows device health. Defines metrics and measurement strategies, analyzes large-scale telemetry, investigates regressions and customer impact, and translates findings into engineering improvements. Establishes reliability, security, observability, and data-quality standards while mentoring engineers and influencing architecture across Windows, data science, security, silicon, OEM, and service teams.

Owns technical workstreams for forward-deployed AVEVA solutions, including configuration, integration, testing, troubleshooting, validation, documentation, and handover in customer environments. Resolves issues involving APIs, data flows, performance, security, deployment, and product gaps. The role also develops reusable integrations and AI patterns, supports demonstrations and acceptance testing, manages risks, and coordinates with customers, architects, peers, and adjacent teams.

Own technical workstreams for forward-deployed industrial software engagements. Configure, integrate, test, troubleshoot, validate, document, and hand over AVEVA solutions using customer systems and data. Resolve complex issues involving APIs, integrations, data flows, security, performance, and deployment. Develop reusable configurations, connectors, templates, AI patterns, and troubleshooting guidance while coordinating with customers and internal teams.

Provides enterprise- and portfolio-level architecture leadership for strategic industrial software engagements. Establishes architecture standards, governance, reference architectures, reusable industry accelerators, and AI adoption principles. Advises senior stakeholders on transformation pathways, operating models, trade-offs, and long-term solution direction across industrial data, OT/IT, cloud, integrations, security, and operations. Resolves complex architecture challenges, validates operational outcomes, informs product and portfolio priorities, and coaches senior architects without direct people-management responsibility.

Owns technical workstreams for forward-deployed AVEVA implementations, including solution configuration, integrations, testing, troubleshooting, validation, documentation, and handover. The role applies customer domain knowledge to industrial systems, APIs, data flows, cloud and edge environments, security, performance, and deployment constraints. It also develops reusable configurations and AI patterns, resolves complex technical issues, supports acceptance and production-readiness activities, coordinates with stakeholders, and escalates risks.