29 Oct
|
Nutanix
|
Bengaluru
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
- 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.
7. 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.
8. 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.
9. 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.
10. 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
- Robust 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
📌 Staff Consulting Architect - AI Infrastructure (Bengaluru)
🏢 Nutanix
📍 Bengaluru