📋 Job Details at a Glance 📍 Location Bengaluru, India 🏢 Company NVIDIA 👥 Experience 5+ years 🎓 Qualification 5+ years. Hands-on with Python, Java, Go, Kubernetes, Terraform. Relevant technical / engineering background 📅 Eligible Batch 2023, 2024, 2025, 2026 📄 Job Type Full Time 💰 Salary Not Disclosed 🏢 Industry Technology 🛠 Key Skills Python Java Go Kubernetes Terraform Senior AI Infrastructure Engineer at NVIDIA — Full Details & How to Apply NVIDIA is seeking a Senior AI Infrastructure Engineer in Bengaluru, India , with direct responsibility for designing and maintaining large-scale production systems using Kubernetes and Terraform . This role is part of NVIDIA ’s ongoing efforts to ensure the reliability and uptime of its GPU cloud services through robust infrastructure engineering. This Senior AI Infrastructure Engineer role is critical for maintaining the performance and availability of NVIDIA ’s production AI systems. The engineer will manage large-scale systems using Kubernetes for container orchestration and Terraform for infrastructure as code, ensuring high efficiency and system stability. These tools are essential for managing the complex, distributed environments that power AI workloads in real-world applications. The job posting confirms that this position is within NVIDIA , a leading technology company focused on AI and GPU cloud services. The role involves hands-on engineering work with production systems, emphasizing technical depth and operational reliability. The team relies on engineers with proven experience in building and managing scalable infrastructure environments. Candidates should have 5+ years experience , with hands-on experience in Python, Java, Go, Kubernetes, and Terraform . They must possess a relevant technical or engineering background . This role is eligible for 2023, 2024, 2025, and 2026 graduates . The salary is Not Disclosed . Applicants will gain practical experience with Python , Java , Go , Kubernetes , and Terraform . This role provides a strong foundation in AI infrastructure engineering, offering a clear path toward advanced system design and cloud operations roles in the tech industry. 📊 Role Difficulty: High | Competition Level: High | Fresher Friendly: No NexisGrow.com features this opportunity as part of its curated tech job listings. All details are sourced from the official job posting. Apply online before the opportunity closes. Job Role & Responsibilities The Senior AI Infrastructure Engineer at NVIDIA designs and deploys scalable, fault-tolerant AI systems using Kubernetes and Terraform , ensuring GPU cloud services maintain 99.99% uptime and meet real-time inference latency targets in production environments. Design and build large-scale production systems using Kubernetes and Terraform to achieve 95% resource utilization efficiency across AI workloads. Maintain and optimize GPU cloud services to ensure 99.99% reliability and reduce average service downtime below 15 minutes per incident. Collaborate with engineering teams to manage capacity planning and performance of internal systems using Java -based microservices and Go -powered APIs. Implement continuous delivery pipelines using Python scripts and Kubernetes CI/CD integrations to reduce deployment cycle time to under 10 minutes. Take ownership of system maintenance and support for AI inference clusters running on NVIDIA DGX hardware, ensuring high availability and zero data loss . Document infrastructure processes and performance improvements using Terraform state management and Python -driven reporting tools to support operational excellence . Troubleshoot production issues in Kubernetes clusters using Go -based monitoring agents and Python diagnostic scripts to resolve incidents within 15 minutes. Optimize containerized application performance using Go and Java services under Kubernetes orchestration to reduce latency by 20% in AI model serving. This role demands direct ownership of system performance, where every decision impacts GPU utilization , service uptime , and end-to-end inference time in real-world AI deployments. Required Skills & Technical Competencies The role requires a strong technical foundation in programming and infrastructure automation, with a focus on Python , Java , Go , Kubernetes , and Terraform as core tools for system design and deployment. Technical Skills Python — advanced — used for scripting automation, monitoring, and performance analysis in Kubernetes and Terraform workflows. Java — intermediate to advanced — used to develop scalable backend services and microservices in cloud environments, especially for GPU service orchestration . Go — advanced — applied to build high-performance, low-latency cloud applications and API gateways for NVIDIA AI services . Kubernetes — advanced — essential for managing containerized AI workloads, including node scheduling , pod autoscaling , and service discovery . Terraform — advanced — used to define and manage infrastructure as code for GPU clusters , ensuring consistent deployment and environment reproducibility . When showcasing these skills, emphasize hands-on experience with Python automation scripts , Kubernetes manifests , and Terraform modules in real AI infrastructure projects. Eligibility Criteria This role is designed for candidates with a relevant engineering background , at least 5 years of hands-on experience , and a proven track record in cloud infrastructure and AI system deployment . Must hold a Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related technical field. Eligible batches are 2023, 2024, 2025, and 2026 only. Minimum of 5 years of professional experience in software engineering or infrastructure roles. CGPA of 3.0 or above is required for admission consideration. Must have a technical portfolio demonstrating experience with Kubernetes , Terraform , or AI infrastructure projects. Must have completed at least one production-grade deployment using Python , Go , or Kubernetes in a real system. Documentation & Portfolio Requirements Your resume must include a detailed section on Python automation scripts , Kubernetes deployment files , and Terraform infrastructure modules used in actual AI or cloud projects. For this role, prioritize showcasing a portfolio with Terraform configurations for GPU clusters , Kubernetes manifests for AI inference pods , and Python scripts that monitor GPU utilization and latency in production. Salary for Senior AI Infrastructure Engineer at NVIDIA 💰 Compensation Not Disclosed The advertised compensation for the Senior AI Infrastructure Engineer role at NVIDIA is Not Disclosed . The exact in-hand figure and any additional components are confirmed by the company during the offer or HR discussion stage. Recommended Courses & Preparation Tips for Senior AI Infrastructure Engineer Upskilling with the right resources significantly enhances your chances of selection for this specialized role in AI infrastructure. Recommended Courses & Resources Python — Essential for scripting and automation in AI infrastructure. Java — Important for building scalable applications in cloud environments. Go — Useful for developing efficient cloud-native applications. Kubernetes — Critical for container orchestration in AI infrastructure management. KodNest — Provides hands-on experience with cloud technologies and infrastructure. Boost your profile with relevant certifications at NexisGrow GetCertified — strengthen your application before you apply. Common Mistakes to Avoid ❌ Applying without demonstrating experience with Kubernetes or Terraform — provide specific examples of your use. ❌ Using a generic resume instead of tailoring it — customize it to the specific responsibilities outlined in the job description. ❌ Listing technologies like Python or Java without detailing their application — include relevant project experiences. ❌ Ignoring the 5+ years of experience requirement — ensure you meet the qualifications before applying. ❌ Adding unrelated technologies to increase ATS keywords — focus on relevant skills only. ❌ Failing to verify the official Apply URL — always check the job details before submission. Before You Apply — Checklist ✅ Confirm the job title, company, location, work mode, and experience requirement. ✅ Check that you meet the supplied qualification criteria. ✅ Verify eligibility against the supplied eligible batches and experience requirement. ✅ Add relevant supplied JD keywords naturally to your resume. ✅ Highlight projects or experience related to the supplied technologies and responsibilities. ✅ Remove unsupported technologies, achievements, or experience claims. ✅ Open and verify the supplied Apply URL before submitting the application. ✅ Save the job details, application date, and role-specific resume version for tracking. Resume Tailoring & ATS Keywords for Senior AI Infrastructure Engineer Optimizing your resume for ATS is crucial for ensuring it gets noticed for this specialized role. ATS Keywords to Include Python Java Go Kubernetes Terraform AI Infrastructure Cloud Services Production Systems Capacity Management Continuous Delivery Deployment System Reliability Resume Tips for This Role Highlight the 5+ years of experience requirement prominently on your resume. Emphasize experience with Kubernetes and Terraform , as they are critical for the role. Include projects that demonstrate skills in building and maintaining cloud infrastructure. Use exact keywords from the job description to enhance ATS alignment without keyword stuffing. Avoid adding unsupported technologies or experiences that cannot be substantiated. Build an ATS-optimized resume for this role using NexisGrow Resume Builder — free for all job seekers. Pro tip: Avoid including unrelated experiences or technologies that do not align with the job requirements. How to Apply for Senior AI Infrastructure Engineer at NVIDIA Apply Now — NVIDIA Open the supplied official Apply URL and confirm the job title and company. Prepare a resume aligned with the supplied skills, responsibilities, qualification, and experience requirements. Complete the official application form accurately and avoid unsupported claims. Submit the application and save the confirmation or application reference if the portal provides one. Record the application date and monitor the official company portal or registered email for updates. Applications reviewed on rolling basis — early submission recommended. Apply Now — NVIDIA NexisGrow.com does not charge any fees for job applications. NVIDIA NVIDIA is a technology company based in Bengaluru, India , offering onsite work opportunities. It specializes in AI and GPU cloud services, with this role embedded in the DGX Cloud group. The team ensures reliability and performance of large-scale production systems. The team designs and maintains large-scale production systems using software and systems engineering. Responsibilities include managing capacity, performance, and deployment through tools like Kubernetes , Terraform , and open-source cloud technologies. Collaboration with engineers and operations teams is central to daily work. This role supports the delivery of internal and external GPU cloud services by ensuring high availability and uptime. It enables hands-on experience with critical cloud infrastructure and system operations in a real-world production environment. Early-career professionals gain exposure to Kubernetes , Terraform , and full systems lifecycle practices. This role offers practical experience in capacity management, deployment, and system reliability—key skills in modern cloud infrastructure. Company Details Industry Technology Location Bengaluru, India Connect With Us Careers Apply Now Website 🔗 nvidia.wd5.myworkdayjobs.com Frequently Asked Questions Interview · Role · Salary · Growth ? Who can apply for this role? Eligibility Tap to expand + Answer Candidates with a relevant technical background and at least 5 years of experience from the eligible batches of 2023, 2024, 2025, and 2026. ? What technical skills are most important for this role? Skills Tap to expand + Answer The most important skills are Python, Java, Go, Kubernetes, and Terraform. ? What is the expected salary for this position? Salary Tap to expand + Answer Salary Display is Not Disclosed. ? What should I expect after applying? Hiring Process Tap to expand + Answer The exact hiring stages are not specified in the job posting. ? How should I prepare for the interview? Preparation Tap to expand + Answer 1. Review Kubernetes and Terraform documentation. 2. Prepare examples of past projects. 3. Understand capacity management principles. 4. Familiarize yourself with NVIDIA's GPU cloud services. 5. Practice problem-solving scenarios related to system reliability. ? Is this role remote, hybrid, or onsite? Work Mode Tap to expand + Answer This role is onsite in Bengaluru, India. ? What career exposure can this role provide? Growth Tap to expand + Answer The role provides exposure to large-scale production systems and advanced cloud technologies, enhancing skills in system reliability and performance management. ? What should I highlight in my resume for this role? Resume Tap to expand + Answer Highlight skills in Kubernetes, Terraform, Python, and Java, along with relevant project experience in cloud infrastructure. ? Is there a last date to apply? Application Tap to expand + Answer Not specified in the job posting. ? Is there a probation period? Probation Tap to expand + Answer Not specified in the job posting. ? What is the work environment like for this role? Culture Tap to expand + Answer The role involves collaboration with engineers and operations teams to ensure system reliability and performance. ? Can freshers apply for this role? Fresher Tap to expand + Answer No, the role requires at least 5 years of experience. 🔗 Explore More on NexisGrow More jobs in Bengaluru Browse all Tech jobs Latest Fresher jobs More jobs at NVIDIA Work From Home jobs Ready to Apply? Don't Miss This Opportunity! Apply only via the official link. NexisGrow.com charges zero application fees. 👉 Apply Now — Official Link NexisGrow.com does not charge any application or registration fees. Beware of fraudulent offers. Naukri ki baat, NexisGrow ke saath • NexisGrow.com