Staff Consulting Architect - AI Infrastructure
Nutanix
Staff Consulting Architect - AI Infrastructure
Professional Services Pune, India Bangalore, India Req.Num.: N2395
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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.
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. Data, Storage & AI Platform Integration
Architect data platforms supporting enterprise AI workloads.
Define integration between AI platforms and:
File services
Object storage
Databa