AI Solutions Architect

About the role

We are looking for an AI Solutions Architect to bridge the gap between technical capabilities and business needs. This generalist role involves conducting in-depth analysis of customer requirements and designing AI solutions that drive business value. The ideal candidate will have strong working knowledge of AI Infrastructure, Gen AI, Agentic Architecture, LLMs, RAG, and Machine Learning.

The role also requires providing technical support to the Pre-Sales team through collaboration on RFPs, RFIs, SOWs, running customer workshops, building rapid prototypes, and presenting technical demos of AI platforms and solutions to existing and potential customers.

Responsibilities

• Evaluate customer requirements and make architectural recommendations for implementation, deployment, and provisioning of AI solutions in on-prem or cloud environments.
• Provide best practice guidance on architectural design across multiple AI applications and projects.
• Propose technology solutions where appropriate that sit outside the core application.
• Drive delivery efforts by evaluating technical trade-offs, clarifying ambiguities, and ensuring that solutions proposed are scalable, reliable, secure, and highly available.
• Support the Pre-Sales team in scoping, qualifying, and developing proposals and opportunities.
• Write high-level and low-level technical documentation for proposed solutions.
• Design and run demos of AI solutions to internal and external audiences.
• Build and integrate simple prototypes using existing AI platforms and tools.

Requirements

Experience:

• 5+ years of experience as a Solutions Architect.
• Hands-on experience with AI agents, RAG pipelines, and LLM inferencing.
• Experience working in AI projects involving at least three of the following domains: AI infrastructure, AI platforms, agentic AI, RAG, and MLOps.

Skills

• Understanding of machine learning, deep learning, neural networks, and foundation models.
• Understanding of AI training and fine-tuning workflows, inference pipelines, and feature engineering.
• Understanding of underlying infrastructure supporting AI workloads, such as GPUs, CPUs, spine/leaf and fat tree topologies, high-speed interconnects, RoCE vs. InfiniBand, high-speed shared storage, GPU-to-GPU and GPU-to-storage communications.
• Understanding of distributed systems requirements and design (scalability, availability, fault tolerance, reliability, consistency).
• Working knowledge of container fundamentals: container networking and storage volumes, as well as building and deploying Docker images.
• Working knowledge of various types of Operating Systems, such as Unix, Linux, and Windows.
• Working knowledge of the Kubernetes ecosystem using Helm charts, operators, and container registries (e.g., Quay).
• Understanding of integration with observability and monitoring (Prometheus, Grafana) and logging.
• Understanding of vGPU, pass-through, MIG, or container-based GPU orchestration options.
• Familiarity with TensorFlow, PyTorch, Rapids, and other GPU-accelerated libraries.
• Familiarity with scripting, Python, Jupyter, Ansible, Terraform, Git, and CI/CD pipelines.
• High-level understanding of hypervisors like ESXi hosts and their management suites.
• High-level understanding of vSAN and VMFS/NFS datastores.
• High-level understanding of database types (SQL and NoSQL) and caching.
• High-level understanding of various storage architectures (SAN, NAS, Object) to recommend the right platform for given workloads.
• High-level understanding of backup/restore, snapshots, and replication strategies aligned with RPO/RTO needs.
• High-level understanding of network fundamentals (VLAN, subnetting, DNS, etc.), L2 vs. L3 networks, routing protocols, and load balancing (F5, NGINX, MetalLB) HA and failover configurations.
• High-level understanding of firewalls, security policies, NAT, VPN tunnels, RBAC, TLS, PKI, and certificates.

Schedule

This position may require evening and weekend work for time-sensitive project implementations.