Senior MLOps Engineer - Computer Vision, Scale-ready

ATI; Woburn (MA); On-site; USD 180,000 - 250,000; Full time

Benefits offered by this job

On-site parking in Woburn

Competitive salary & comprehensive ben

Career growth in ML platform

Job summary

Automated Tire (ATI) is seeking a Staff MLOps Engineer in Woburn, MA to own the end-to-end production ML stack for BrakeWise, including serving infrastructure, deployment, monitoring, and retraining loops. You will drive architecture decisions, scalability, and reliability to meet real-world throughput and cost targets.

The role requires deep experience with computer vision pipelines, GCP stack, and production ML operations, collaborating with mobile app, dashboard teams, and hardware ops to

Qualifications

• 8+ years of professional engineering experience, including ownership of ML systems in production.
• Demonstrated experience taking a computer vision pipeline from prototype to production scale for real users.
• Deep experience deploying segmentation and classification models in multi-stage pipelines.

Responsibilities

• Own the multi-stage inference pipeline end to end — serving architecture, latency, throughput, reliability, and cost per inspection.
• Re-architect the pipeline from Cloud Functions MVP to scalable infrastructure (containerized inference, GPU-backed serving, batching, autoscaling).
• Own model deployment: versioning, staged rollout, canary/shadow evaluation, fast rollback.

Skills

ML systems in production

Computer vision pipelines

GCP Vertex AI

Docker

Python

PyTorch/TensorFlow

CI/CD automation

Terraform

Incident response

Tools

Vertex AI

Cloud Run

GKE

Cloud Functions

Docker

Terraform

Job description

Automated Tire (ATI) is seeking a Staff MLOps Engineer in Woburn, MA to own the end-to-end production ML stack for BrakeWise, including serving infrastructure, deployment, monitoring, and retraining loops. You will drive architecture decisions, scalability, and reliability to meet real-world throughput and cost targets.

The role requires deep experience with computer vision pipelines, GCP stack, and production ML operations, collaborating with mobile app, dashboard teams, and hardware ops to