Enterprise Architect – AI

Overview

In this role you will own end-to-end AI project delivery—from silicon and data-center design to software, MLOps, and governance for trustworthy AI at scale. You will lead technically, mentor teams, and partner across sales, pre-sales, delivery, and NVIDIA/hyperscaler ecosystems. The role spans GPU infrastructure, high-performance networking, model training and inference, and risk/governance to shape the AI platform and operating model. This is a hands-on, architecture-focused position with measurable impact on client outcomes and the firm’s AI perspective.

Responsibilities

• Own end-to-end technical delivery of AI/ML engagements: architecture definition, design authority, build oversight, and go-live validation
• Chair Architecture Review Board (ARB) and Technical Design Authority (TDA) sessions, manage governance gates and design sign-off
• Architect AI infrastructure including GPU/accelerator compute, networking, and storage
• Design the AI software stack: training/finetuning pipelines, distributed training, inference/serving, MLOps/LLMOps, vector databases, RAG and agentic architectures
• Define AI governance frameworks covering model risk, responsible AI, data lineage, bias testing, explainability, and regulatory alignment
• Advise client executives on platform strategy, build-vs-buy, and AI operating model design
• Lead workshops, design sessions, and PoCs with cross-functional teams
• Serve as technical escalation point and unblock delivery issues under time pressure
• Mentor other architects/engineers to uplift AI depth across the practice
• Partner with sales/pre-sales to scope solutions and validate feasibility
• Define automation, orchestration, and observability standards across the AI stack
• Architect integrations between AI platforms and enterprise networks/third-party environments
• Stay current with AI advances and translate into delivery methodology and recommendations

Key requirements

• 10+ years in enterprise architecture, infrastructure engineering, or platform engineering
• 5+ years focused on AI/ML systems design and delivery, including at least 2 years with generative AI/LLM workloads
• Demonstrated track record leading technical delivery on enterprise-scale AI or HPC infrastructure programmes
• GPU/accelerator architectures (NVIDIA/AMD) and multi-node scale-out
• Experience with NVLink/NVSwitch, InfiniBand/RoCEv2, 400G/800G Ethernet
• Storage systems for training/checkpointing workloads (parallel/high-throughput file systems)
• Kubernetes, Docker, Slurm, Run:ai or equivalent GPU scheduling
• PyTorch and TensorFlow hands-on; Horovod/DeepSpeed/Megatron-LM or similar
• Distributed training and inference/serving platforms (TensorRT/LLM tooling)
• MLOps: Kubeflow, MLflow, plus at least one hyperscaler ML platform (SageMaker/Azure ML/Vertex AI)
• Generative AI tech: LoRA/QLoRA, RAG, vector databases (Pinecone/Milvus/Weaviate), LangChain/LangGraph
• Data pipelines and data lakehouse architectures; data quality/ lineage tooling
• Terraform/Ansible, GitOps (ArgoCD); Kubeflow Pipelines, Airflow or Argo Workflows
• NVIDIA DCGM, Prometheus/Grafana, OpenTelemetry; ELK/OpenSearch logging and tracing

• Excellent executive communication and presentation skills
• Ability to work across sales, delivery, and partner teams
• Leadership with mentoring and credibility with client stakeholders
• GPU/accelerator architectures and scale-out design
• High-performance networking and data center design
• Parallel/high-throughput storage

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