Senior Solutions Architect — Houston, TX

Senior AI Architect, Pre-Sales & Customer Solutions

Location: Houston, TX (hybrid, 2 days on site)

Employment type: Full-time, permanent

About the opportunity

Our client is an established technology integrator with decades of experience helping enterprises modernize their IT environments. As demand for AI grows across their customer base, they are building out a dedicated AI practice and are looking for a senior architect to help lead it.

In this role, you will be the technical voice between customers and delivery teams. You will listen to what a business is trying to achieve, then turn it into a practical, secure and scalable AI architecture, from infrastructure to application layer. You’ll work closely with the sales organization, so strong communication skills matter as much as technical depth.

What you’ll do

• Meet with customers to understand their goals, constraints and existing environment, and translate them into clear architecture recommendations

• Design AI solutions that run on-premises, in the cloud, or in hybrid setups, and explain the trade-offs behind each choice

• Guide teams on best practices across several AI initiatives running in parallel

• Support the sales team by scoping opportunities, qualifying leads and contributing to proposals, RFP/RFI responses and statements of work

• Lead customer workshops and deliver live demonstrations of AI platforms and solutions

• Build quick prototypes to prove value before full implementation

• Produce high-level and detailed design documentation for proposed solutions

• Recommend complementary technologies when the customer’s needs go beyond the core solution

• Make sure every design is reliable, secure and ready to scale

What we’re looking for

• 5+ years in a Solutions Architect or similar customer-facing architecture role

• Hands-on experience with generative AI, including LLMs, RAG and AI agents

• Experience delivering AI projects across more than one layer of the stack, such as infrastructure, platforms, applications or ML operations

• Solid grasp of machine learning and deep learning fundamentals, and familiarity with frameworks such as PyTorch or TensorFlow

• Understanding of the infrastructure behind AI workloads, including GPU environments and the compute, networking and storage that support them

• Experience with containers and Kubernetes, plus automation and DevOps practices (Python, infrastructure as code, CI/CD)

• Working knowledge of cloud and on-premises environments, including virtualization, storage and networking fundamentals

• Strong communication skills, comfortable presenting to both technical and business audiences

Technical skills

• AI/ML: solid understanding of machine learning, deep learning and foundation models, including training, fine-tuning, inference and feature engineering. Familiarity with TensorFlow, PyTorch and GPU-accelerated libraries such as RAPIDS

• GPU & infrastructure: knowledge of GPU sharing and orchestration approaches (vGPU, pass-through, MIG, containerized GPUs), high-speed networking (InfiniBand, RoCE), high-performance shared storage, and the principles of distributed systems

• Containers & Kubernetes: Docker, Kubernetes, Helm, operators and container registries; Linux, Unix and Windows environments; monitoring with tools like Prometheus and Grafana

• Automation & DevOps: Python, Jupyter, Terraform, Ansible, Git and CI/CD

• Virtualization, storage & data: VMware environments, SAN/NAS/object storage, SQL/NoSQL databases and caching, and backup and recovery planning (RPO/RTO)

• Networking & security: VLANs, subnetting, DNS, Layer 2/3, load balancing, high availability, firewalls, VPN, RBAC and TLS/PKI