Solutions Architect

Teqconnect; New York (NY); On-site; USD 120,000 - 190,000; Full time

Job summary

Teqconnect is seeking a Solutions Architect for its US-based AI infrastructure team. This is a highly technical, customer-facing role working with strategic enterprise customers to design, build, deploy and optimise large-scale production AI systems.

You will own customers from technical discovery and POCs through deployment, optimisation and adoption, acting as a trusted technical advisor to Engineering, Product and Sales.

Qualifications

• Strong pre-sales and post-sales experience, owning customers from technical discovery and POCs through deployment, optimisation and adoption.
• Comfortable acting as a trusted technical advisor to enterprise customers and working closely with Engineering, Product and Sales.
• Deep expertise in at least ONE of the following: LLM inference & post-training, HPC / GPU infrastructure, or database performance engineering.

Responsibilities

• Own customers from technical discovery and POCs through deployment and adoption.
• Act as trusted technical advisor to enterprise customers with cross-functional collaboration.
• Design, build, deploy and optimise large-scale production AI systems.

Skills

Pre-sales experience

Post-sales experience

Enterprise customer engagement

POC deployment

Tools

TensorRT-LLM

vLLM

GPU clusters

Job description

We’re partnering exclusively with a high-growth AI infrastructure company that is rapidly expanding its US Solutions Architecture organisation. This is a technical, customer-facing role working with strategic enterprise customers to design, build, deploy and optimise large-scale production AI systems.

What we’re looking for:

• Strong pre-sales and post-sales experience, owning customers from technical discovery and POCs through deployment, optimisation and adoption.
• Comfortable acting as a trusted technical advisor to enterprise customers and working closely with Engineering, Product and Sales.

Deep expertise in at least ONE of the following:

• LLM inference & post-training including vLLM, TensorRT-LLM, SGLang, fine-tuning or model optimisation.
• HPC / GPU infrastructure including distributed compute, GPU clusters and performance optimisation.
• Database performance engineering including query optimisation, sharding, distributed systems and high-performance data infrastructure.