Staff Consulting Architect - AI Infrastructure

Nutanix is seeking a Staff Consulting Architect – AI to provide senior technical leadership in the architecture, design, and transformation of enterprise AI platforms based on the Nutanix AI Platform (NAI) and equivalent enterprise AI/ML platforms.

The role is intended for a highly experienced AI platform architect who can lead complex enterprise AI transformation initiatives spanning AI infrastructure, GPU-enabled Kubernetes, MLOps/LLMOps, Generative AI, RAG, model serving, data services, automation, security, and hybrid/multi-cloud environments.

The Sr. Staff Consulting Architect will act as a trusted technical advisor to senior customer stakeholders, helping organizations define their AI platform strategy, target architecture, adoption roadmap, and operating model.

The role requires a combination of deep hands-on expertise and strategic architecture leadership across AI/ML platforms, Kubernetes, virtualization, GPU infrastructure, cloud-native technologies, data platforms, and automation.

• Enterprise AI Strategy & Architecture
• Lead architecture strategy for enterprise AI/ML and Generative AI platform transformation programs.
• Define target-state AI platform architectures aligned to customer business, data, application, security, and operational requirements.
• Architect enterprise AI platforms across:
• Nutanix AI Platform (NAI)
• Kubernetes-based AI platforms
• Nutanix AHV
• Bare-metal GPU infrastructure
• Hybrid and multi-cloud environments
• Public cloud AI/ML platforms where applicable
• Develop phased AI adoption roadmaps covering:
• AI infrastructure
• Model development
• Model training and fine-tuning
• Inference
• RAG
• MLOps/LLMOps
• Application integration
• Governance and operations
• Advise customer executives, enterprise architects, data scientists, ML engineers, platform teams, and application teams on enterprise AI adoption.
• Translate business use cases into scalable, secure, resilient, and operationally sustainable AI architectures.
• Evaluate architecture trade-offs across performance, scalability, GPU utilization, cost, security, data locality, and operational complexity.
• AI Infrastructure & GPU Platform Architecture
• Design enterprise AI infrastructure supporting:
• GPU-enabled Kubernetes
• AI/ML workloads
• Model training
• Fine-tuning
• Inference
• Vector search
• RAG workloads
• Define GPU architecture considering:
• GPU selection and sizing
• GPU scheduling
• GPU partitioning and sharing
• CPU/GPU topology
• NUMA
• Memory requirements
• Network bandwidth
• Storage throughput
• Architect high-performance AI infrastructure across virtualized and bare-metal environments.
• Design GPU-enabled Kubernetes platforms using technologies such as:
• NVIDIA GPU Operator
• NVIDIA CUDA
• NVIDIA Container Toolkit
• NVIDIA Triton Inference Server
• KServe
• Evaluate and architect high-performance networking for AI workloads, including where applicable:
• RDMA
• InfiniBand
• RoCE
• High-speed Ethernet
• NVIDIA networking technologies
• Optimize infrastructure for GPU utilization, workload density, performance, availability, and cost.
• Define architectures for scalable AI compute pools and shared enterprise AI platforms.
• Kubernetes & AI Platform Engineering
• Architect Kubernetes platforms optimized for enterprise AI workloads.
• Define AI platform architecture across:
• Kubernetes control plane
• GPU worker nodes
• Storage
• Networking
• Ingress
• Security
• Observability
• Design multi-cluster and multi-tenant AI environments.
• Define workload isolation across:
• Cluster
• Namespace
• Node
• GPU
• Architect integration with enterprise infrastructure and services including:
• Identity and access management
• LDAP/Active Directory
• DNS
• PKI/certificates
• Load balancers
• Storage
• Backup and DR
• Monitoring and logging
• Drive platform engineering and self-service capabilities for data scientists and AI developers.
• Define standardized patterns for AI workspace provisioning and lifecycle management.
• Generative AI, RAG & AI Application Architecture
• Lead architecture for enterprise Generative AI solutions and platforms.
• Design end-to-end RAG architectures covering:
• Data ingestion
• Document processing
• Chunking
• Embedding generation
• Vector databases
• Retrieval
• Re-ranking
• Prompt orchestration
• LLM inference
• Architect enterprise AI application patterns using technologies such as:
• Large Language Models (LLMs)
• Embedding models
• Vector databases
• LangChain
• LlamaIndex
• NVIDIA NeMo
• NVIDIA NIM
• Triton
• KServe
• Define architectures for:
• LLM inference
• Model serving
• Fine-tuning
• RAG
• AI agents
• Enterprise copilots
• Evaluate open-source and commercial AI models and frameworks based on customer requirements.
• Design model deployment strategies considering:
• Performance
• Latency
• Throughput
• GPU utilization
• Model size
• Security
• Data privacy
• Cost
• Guide customers in moving AI prototypes and proof-of-concepts into production-grade platforms.
• MLOps, LLMOps & Automation
• Define enterprise MLOps and LLMOps architectures covering the complete AI lifecycle.
• Design workflows for:
• Data preparation
• Model training
• Fine-tuning
• Model evaluation
• Model registration
• Model deployment
• Model monitoring
• Model retirement
• Integrate AI platforms with CI/CD and GitOps practices.
• Design automated AI platform provisioning using:
• Terraform
• Ansible
• Kubernetes operators
• APIs
• Establish automation for:
• GPU infrastructure provisioning
• Kubernetes clusters
• AI workspaces
• Model deployment
• Inference endpoints
• RAG pipelines
• Integrate automation with:
• Nutanix APIs
• NKP APIs
• Kubernetes APIs
• NVIDIA technologies
• Cloud provider APIs
• Develop repeatable, version-controlled, auditable, and supportable AI platform delivery patterns.
• Data, Storage & AI Platform Integration
• Architect data platforms supporting enterprise AI workloads.
• Define integration between AI platforms and:
• File services
• Object storage
• Databases
• Data lakes
• Vector databases
• Enterprise data sources
• Design storage architectures optimized for:
• Model datasets
• Training data
• Model artifacts
• Checkpoints
• Embeddings
• Vector data
• Inference workloads
• Address performance requirements across:
• IOPS
• Throughput
• Latency
• Metadata performance
• Network bandwidth
• Define data movement and data locality strategies for hybrid AI environments.
• Establish architecture patterns for data protection, backup, disaster recovery, and lifecycle management.
• AI Security, Governance & Responsible AI
• Define security architecture across the AI infrastructure, platform, data, model, and application layers.
• Establish controls for:
• Identity and access management
• RBAC
• Secrets management
• Network security
• Data protection
• Model access
• API security
• Design secure multi-tenant AI platforms with appropriate isolation.
• Define governance for:
• Models
• Datasets
• Prompts
• Embeddings
• AI applications
• Inference endpoints
• Establish security and governance patterns for enterprise RAG and GenAI applications.
• Address risks associated with:
• Sensitive data
• Data leakage
• Prompt injection
• Model misuse
• Unauthorized model access
• Integrate policy-as-code and security controls into AI platform lifecycle management.
• Guide customers toward responsible, auditable, and governed enterprise AI adoption.
• Observability, Performance & Cost Optimization
• Define comprehensive observability for AI platforms covering:
• Infrastructure
• GPUs
• Kubernetes
• Models
• Inference services
• Applications
• Establish monitoring and optimization for:
• GPU utilization
• GPU memory
• CPU utilization
• Network performance
• Storage throughput
• Model latency
• Inference throughput
• Define AI platform SLOs, SLIs, and operational KPIs.
• Troubleshoot complex issues across:
• GPU infrastructure
• Kubernetes
• Networking
• Storage
• AI frameworks
• Model serving
• Application layers
• Drive capacity planning and AI infrastructure optimization.
• Develop strategies for optimizing AI infrastructure and cloud consumption costs.
• Consulting, Technical Leadership & Customer Enablement
• Lead executive-level architecture workshops for:
• Enterprise AI strategy
• AI platform adoption
• Generative AI
• RAG
• MLOps/LLMOps
• AI infrastructure modernization
• Act as the senior technical advisor to customer:
• CIO/CTO organizations
• Enterprise architects
• Data and AI leaders
• Infrastructure leaders
• Platform engineering teams
• Data science and ML engineering teams
• Lead complex technical discovery and architecture engagements.
• Provide technical direction for strategic AI programs and high-value customer engagements.
• Act as the senior escalation point for complex AI architecture and implementation challenges.
• Review and govern AI architectures developed by Staff Consulting Architects and Consultants.
• Mentor architects and consultants and build organizational AI capability.
• Reference Architecture, Innovation & Thought Leadership
• Create enterprise-grade AI reference architectures and solution blueprints.
• Develop reusable:
• Design patterns
• Implementation guides
• Automation frameworks
• AI solution templates
• MLOps/LLMOps patterns
• RAG reference implementations
• Identify and evaluate emerging AI technologies and their applicability to Nutanix solutions.
• Drive innovation across:
• Generative AI
• AI agents
• RAG
• GPU infrastructure
• AI platform engineering
• Model serving
• AI observability
• Collaborate with Product Management, Engineering, Support, and other technical teams to provide field feedback and influence product direction.
• Contribute to technical whitepapers, solution briefs, blogs, reference architectures, and internal enablement content.
• Represent Nutanix in strategic customer discussions, technical forums, and industry events where appropriate.

Technical Skills & Experience

Mandatory Skills – Must Have

• 10+ years of experience across infrastructure, cloud, virtualization, Kubernetes, AI/ML platforms, or related technologies.
• Proven experience architecting and delivering enterprise AI/ML platforms and AI infrastructure.
• Deep understanding of AI/ML platform architecture and enterprise AI adoption.
• Strong hands-on experience with:
• Kubernetes
• Nutanix AHV or equivalent virtualization platforms
• GPU-enabled infrastructure
• Linux
• Container technologies
• Strong experience with Nutanix AI Platform (NAI) or equivalent enterprise AI platforms.
• Deep understanding of GPU architecture and AI workload requirements.
• Strong understanding of:
• GPU scheduling
• CUDA
• GPU Operator
• GPU resource management
• GPU performance optimization
• Strong experience with Kubernetes-based AI platforms.
• Strong understanding of AI/ML frameworks and platforms, including several of:
• NVIDIA NeMo
• NVIDIA NIM
• Triton
• KServe
• Kubeflow
• MLflow
• Strong understanding of Generative AI and LLM architectures.
• Proven experience designing RAG architectures and AI application platforms.
• Strong understanding of:
• LLMs
• Embedding models
• Vector databases
• Model serving
• Fine-tuning
• Inference
• Strong experience with Infrastructure as Code:
• Terraform
• Ansible
• Strong experience with CI/CD, GitOps, and platform automation.
• Strong understanding of enterprise storage, networking, security, and data architecture for AI workloads.
• Strong troubleshooting skills across infrastructure, Kubernetes, GPU, networking, storage, and AI platform layers.
• Proven ability to lead complex customer engagements and architecture decisions.
• Excellent customer-facing, consulting, presentation, and executive communication skills.

Added Advantage – Good to Have

• Experience with NVIDIA AI Enterprise.
• Experience with NVIDIA DGX, HGX, or equivalent GPU infrastructure.
• Experience with NVIDIA networking technologies, InfiniBand, or RoCE.
• Experience with GPU partitioning and sharing technologies.
• Experience with Kubeflow, MLflow, KServe, Ray, or equivalent AI/ML platforms.
• Experience with LangChain, LlamaIndex, Flowise, or equivalent GenAI orchestration frameworks.
• Experience with vector databases such as Milvus, Qdrant, Weaviate, pgvector, or equivalent.
• Experience with AI agents and agentic architectures.
• Experience with multimodal AI.
• Experience with AI/ML model fine-tuning and optimization.
• Experience with public cloud AI services and GPU platforms on AWS or Azure.
• Experience with hybrid and multi-cloud AI architectures.
• Experience with AI governance, security, and responsible AI practices.
• Experience with FinOps and AI infrastructure cost optimization.

Trainable / Developable Skills

• Advanced AI agent and agentic workflow architectures.
• Emerging LLM and multimodal model architectures.
• Advanced model optimization and inference technologies.
• Emerging GPU and AI accelerator technologies.
• Advanced AI governance and responsible AI frameworks.
• Advanced AI observability and LLM evaluation frameworks.
• New Nutanix AI platform capabilities and ecosystem integrations.

Education & Certifications

Education

• Bachelor's Degree in Engineering, Computer Science, Artificial Intelligence, Data Science, or equivalent discipline.
• Master's degree in Computer Science, AI/ML, Data Science, or a related discipline is an advantage.

Preferred Certifications

• Nutanix certifications:
• NCP
• NAI / relevant Nutanix AI certification
• NCX preferred
• Certified Kubernetes Administrator (CKA)
• Certified Kubernetes Application Developer (CKAD)
• NVIDIA certifications relevant to AI/ML or data center technologies
• Terraform Associate
• Relevant AWS/Azure certifications
• Relevant AI/ML or cloud architecture certifications

Location & Work Shifts

• Pune / Bangalore – Hybrid model (3 days a week)
• Travel as required for customer assignments
• Willingness to work US and EMEA shifts based on customer and engagement requirements

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