APAC AI Solutions Engineer — Field Deployments

Superb AI Inc.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Superb AI Inc. in Singapore is seeking the first regional engineer to own the technical side of customer engagements.

You will validate feasibility during sales, tailor deployments, and provide hands-on support on site and remotely across APAC. Working with our ZERO vision foundation model, MLOps data curation, labeling, retraining, and a video analytics layer, you will split time between pre-sales and delivery, run workshops, fine-tune models on live projects, and build reference

Qualifications

• 3+ years hands-on experience in computer vision or ML, with real customer-facing work
• Fine-tuned detection or segmentation models on real-world data
• Honest evaluation: report per-class precision/recall and say when not working
• Comfort debugging on Linux, Docker, or GPU servers
• Strong writing; feasibility write-ups read by customers
• Stay composed in front of customers when numbers miss targets
• Fluent English for global communications

Responsibilities

• Prove what works before we sell it: run feasibility studies and lead technical workshops with customers and partners
• Fine-tune and deploy models on live projects; collect field data, retrain, verify, and roll out
• Build reference implementations and train local SI partners to deliver independently
• Turn field learnings into feedback for HQ product and research teams
• First 90 days: shadow active engagements, run a full feasibility study end-to-end, and lead a customer workshop

Skills

Computer vision

ML engineer

Customer-facing

Linux

English fluency

Feasibility analysis

On-site calm

Tools

TensorRT

DeepStream

Jetson

VSS

Docker

Job description

Superb AI Inc. in Singapore is seeking the first regional engineer to own the technical side of customer engagements.

You will validate feasibility during sales, tailor deployments, and provide hands-on support on site and remotely across APAC. Working with our ZERO vision foundation model, MLOps data curation, labeling, retraining, and a video analytics layer, you will split time between pre-sales and delivery, run workshops, fine-tune models on live projects, and build reference